Research On The Immunogenetic Mechanism And Risk Prediction For Ankylosing Spondylitis Onset

Ankylosing spondylitis is a progressive chronic disease characterized byinflammatory lower back pain, frequently accompanied by peripheral arthritis, enthesis andiritis, and even spinal deformity and ankylosis. Similar to the incidence in populations ofEuropean ancestry, the prevalence of ankylosing spondylitis is0.24%in the Chinesepopulation. Individuals with ankylosing spondylitis have a high prevalence of work-relateddisabilities, ranging from4%at5years after disease diagnosis to50%at45years afterdiagnosis.AS is a polygenic disease with complex genetic background. In order to get a morecomprehensive understanding of the etiology, risk of developing and progress mechanism,AS need to be researched in the different levels such as gene, transcription and translationand so on.This study is aimed to explore the immunogenetic pathogenesis of AS intranscriptome (gene expression profile) and risk prediction of the onset respectively.Part I: Research on the immunogenetic mechanism of ankylosing spondylitis basedon RNA-Seq technologyObjective: To identify differentially expressed genes in peripheral blood cells fromChinese Han patients with ankylosing spondylitis (AS) compared with healthy individuals,providing advanced clues for the immune genetic mechanisms of AS in Chinese Hanpatients.Methods: RNA was extracted from peripheral blood cells collected from7patients withactive disease and7gender-matched and age-matched controls. Expression profiles ofthese cells were determined using RNA-Seq technology. Candidate genes with differentialexpressions were confi rmed in the same samples. These genes were then validated in adifferent sample cohort of55patients with AS and55controls by quantitative reversetranscription-PCR (qRT-PCR) and AB3730DNA Analyzer. KEGG Pathway and GOanalysis were taken respectively for the candidate genes with differences expression over1.5log2(Fold_change).Results: RNA-Seq analysis identified7767genes differentially expressed between patientswith AS and controls (among2221genes upregulated,26genes were upregulated up to1.5 log2(Fold_change); among5546genes downregulated,57genes were downregulated up to1.5the log2(Fold_change)). Among the33genes reported to be associated with AS,31were found to be expressed and17genes upregulated obviously. A total of753genes with4137SNP were detected. Combining differential gene expression levels and the knownbiological functions,10candidate genes were identified. These genes were furthervalidated in a different sample cohort of55patients with AS and55controls byquantitative reverse transcription-PCR (qRT-PCR) and AB3730DNAAnalyzer.Underexpression of CD69(р<0.01), ERAP2(р<0.01), NFKBIA (р<0.01),TNFR2(р<0.05) and orverexpression of SPOCK2(р<0.01) was confirmed. GO analysisand KEGG Pathway analysis were taken for the26genes upregulated up to1.5the log2(Fold_change) and found that the enrichment in the regulation of apoptosis, programmeddeath process and the apoptotic pathway. GO analysis and KEGG Pathway analysis weretaken for the57genes downregulated up to1.5the log2(Fold_change) and found that theenrichment in cell surface signal transduction, defense response, immune response, andToll-like receptor signaling pathway.Conclusion: In addition to CD69, ERAP2, NFKBIA, TNFR2gene-mediated autoimmuneand inflammatory models, the SPOCK2mediated cartilage metabolism and bone formationin AS pathological processes may play an important role.Part II: Research on the risk prediction for AS onset based on GWASObjective: To establish the risk prediction model of AS onset based on GWAS data fromthe US (United States) and UK (United Kingdom) samples and revalue the model in theChinese Han.Methods: By using GWAS SNPs data from the US and UK samples, filter out the SNPswith P-value <0.05by correlation analysis, and acquire the optimal combination of1to5SNPs by variable selection methods. The risk prediction model of AS is established afterlinkage disequilibrium analysis. Bayes Discriminant Analysis and Logistic RegressionAnalysis combined with cross-validation analysis are taken for the US and UK samplesand the prediction rate are obtained. The risk prediction rate by the model is revalued inCHB samples after genetic stratification test by Structure2.0software in all samples.Results: A total of290,372SNPs were get from the GWAS SNPs data of the US (900cases,3789controls) and UK (1153cases,1351controls) samples. Filtering the SNPs with P-value <0.05by correlation analysis, the optimal combination of1to5SNPs (rs3915971,rs9266825, rs3128982, rs2844505, rs2248462) screening by variable selection methodswas acquired. The risk prediction model of AS onset is established after linkagedisequilibrium analysis. Bayes Discriminant Analysis and Logistic Regression Analysiscombined with cross-validation analysis were taken for US and UK samples and obtainedthe prediction rate, Bayes Discriminant Analysis (1～5SNPs): the harmonic mean (HMSS)67.56%～89.14%; logistic the Regression Analysis (1～5SNPs): HMSS:79.95%～87.95%. The genetic stratification test for the5SNPs distribution by Structure2.0softwarein US, UK and CHB (960cases,945controls) samples reveals no obvious stratification.The risk prediction rate by the model using Bayes discriminant analysis and LogisticRegression methods combined with cross-validation was revalued in CHB samples and theresults showed that Bayesian discriminant analysis (1～5SNPs): Accuracy69.61%～74.62%, HMSS68.90%～74.60%; Logistic Regression Analysis (1～5SNPs): Accuracy69.61%～74.84%, HMSS:68.90%～74.83%.Conclusion: The risk prediction model of AS onset based on GWAS data from the US andUK samples is efficient, and revalued well in the Chinese Han.