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MICA019* Allele and Soluble MICA as Biomarkers for Ankylosing Spondylitis in Taiwanese.

ABSTRACT: MICA (major histocompatibility complex class I chain-related gene A) interacts with NKG2D on immune cells to regulate host immune responses. We aimed to determine whether MICA alleles are associated with AS susceptibility in Taiwanese. MICA alleles were determined through haplotype analyses of major MICA coding SNP (cSNP) data from 895 AS patients and 896 normal healthy controls in Taiwan. The distributions of MICA alleles were compared between AS patients and normal healthy controls and among AS patients, stratified by clinical characteristics. ELISA was used to determine soluble MICA (sMICA) levels in serum of AS patients and healthy controls. Stable cell lines expressing four major MICA alleles (MICA*002, MICA*008, MICA*010 and MICA*019) in Taiwanese were used for biological analyses. We found that MICA*019 is the only major MICA allele significantly associated with AS susceptibility (P_FDR = 2.25 × 10^-115; OR, 14.90; 95% CI, 11.83-18.77) in Taiwanese. In addition, the MICA*019 allele is associated with syndesmophyte formation (P_FDR = 0.0017; OR, 1.69; 95% CI, 1.29-2.22) and HLA-B27 positivity (P_FDR = 1.45 × 10^-33; OR, 28.79; 95% CI, 16.83-49.26) in AS patients. Serum sMICA levels were significantly increased in AS patients as compared to healthy controls. Additionally, MICA*019 homozygous subjects produced the highest levels of sMICA, compared to donors with other genotypes. Furthermore, in vitro experiments revealed that cells expressing MICA*019 produced the highest level of sMICA, as compared to other major MICA alleles. In summary, the MICA*019 allele, producing the highest levels of sMICA, is a significant risk factor for AS and syndesmophyte formation in Taiwanese. Our data indicate that a high level of sMICA is a biomarker for AS.

SUBMITTER: Wang CM

PROVIDER: S-EPMC8235541 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAnkylosing spondylitis (AS) is an autoimmune disease affecting mainly spine and sacroiliac joints and adjacent soft tissues. Genome-wide association studies (GWASs) are used to evaluate genetic associations and to predict genetic risk factors that determine the biological basis of disease susceptibility. We aimed to explore the race-specific SNP susceptibility of AS in Taiwanese individuals and to investigate the association between HLA-B27 and AS susceptibility SNPs in Taiwan.MethodsGenotyping data were collected from a medical center participating in the Taiwan Precision Medicine Initiative (TPMI) in the northern district of Taiwan. We designed a case-control study to identify AS susceptibility SNPs through GWAS. We searched the genome browser to find the corresponding susceptibility genes and used the GTEx database to confirm the regulation of gene expression. A polygenic risk score approach was also applied to evaluate the genetic variants in the prediction of developing AS.ResultsThe results showed that the SNPs located on the sixth chromosome were related to higher susceptibility in the AS group. There was no overlap between our results and the susceptibility SNPs found in other races. The 12 tag SNPs located in the MHC region that were found through the linkage disequilibrium method had higher gene expression. Furthermore, Taiwanese people with HLA-B27 positivity had a higher proportion of minor alleles. This might be the reason that the AS prevalence is higher in Taiwan than in other countries. We developed AS polygenic risk score models with six different methods in which those with the top 10% polygenic risk had a fivefold increased risk of developing AS compared to the remaining group with low risk.ConclusionA total of 147 SNPs in the Taiwanese population were found to be statistically significantly associated with AS on the sixth pair of chromosomes and did not overlap with previously published sites in the GWAS Catalog. Whether those genes mapped by AS-associated SNPs are involved in AS and what the pathogenic mechanism of the mapped genes is remain to be further studied.

Project description:ObjectiveThe aim of this study was to search for key genes in ankylosing spondylitis (AS) through comprehensive bioinformatics analysis, thus providing some theoretical support for future diagnosis and treatment of AS and further research.MethodsGene expression profiles were collected from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/ ) by searching for the term "ankylosing spondylitis". Ultimately, two microarray datasets (GSE73754 and GSE11886) were downloaded from the GEO database. A bioinformatic approach was used to screen differentially expressed genes and perform functional enrichment analysis to obtain biological functions and signalling pathways associated with the disease. Weighted correlation network analysis (WGCNA) was used to further obtain key genes. Immune infiltration analysis was performed using the CIBERSORT algorithm to conduct a correlation analysis of key genes with immune cells. The GWAS data of AS were analysed to identify the pathogenic regions of key genes in AS. Finally, potential therapeutic agents for AS were predicted using these key genes.ResultsA total of 7 potential biomarkers were identified: DYSF, BASP1, PYGL, SPI1, C5AR1, ANPEP and SORL1. ROC curves showed good prediction for each gene. T cell, CD4 naïve cell, and neutrophil levels were significantly higher in the disease group than in the paired normal group, and key gene expression was strongly correlated with immune cells. CMap results showed that the expression profiles of ibuprofen, forskolin, bongkrek-acid, and cimaterol showed the most significant negative correlation with the expression profiles of disease perturbations, suggesting that these drugs may play a role in AS treatment.ConclusionThe potential biomarkers of AS screened in this study are closely related to the level of immune cell infiltration and play an important role in the immune microenvironment. This may provide help in the clinical diagnosis and treatment of AS and provide new ideas for further research.

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MICA019* Allele and Soluble MICA as Biomarkers for Ankylosing Spondylitis in Taiwanese.

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Dataset Information

MICA*019 Allele and Soluble MICA as Biomarkers for Ankylosing Spondylitis in Taiwanese.

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OmicsDI is part of the ELIXIR infrastructure

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MICA019* Allele and Soluble MICA as Biomarkers for Ankylosing Spondylitis in Taiwanese.