【作者】 张东辉；

【导师】 李福祯；

【作者基本信息】 郑州大学 ， 眼科学（专业学位）， 2025， 硕士

【摘要】 急性前葡萄膜炎(acute anterior uveitis,AAU)与人类白细胞抗原(human leukocyte antigen,HLA)-B27密切相关,可以伴发或不伴发于系统性疾病。其伴发的系统性疾病主要是血清阴性脊柱关节炎(spondyloarthritis,SpA),以强直性脊柱炎(ankylosing spondylitis,AS)、炎症性肠病(inflammatory bowel disease,IBD,包括溃疡性结肠炎和克罗恩病)和银屑病关节炎(psoriatic arthritis,PsA)为代表,这些系统性疾病本身就与HLA-B27密切相关。可以认为HLA-B27是这类疾病共同的遗传背景,但是,非HLA-B27遗传因素、免疫因素及环境因素在这类疾病的发生中也起重要作用。近年来,生物信息学技术的迅猛发展以及各类组学数据库的不断更新,例如基因表达综合数据库(gene expression omnibus,GEO)和全基因组关联分析(genome-wide association study,GWAS)数据库,为研究者对AAU和HLA-B27相关性疾病进行联合分析和多组学分析提供了可能。我们希望利用生物信息学方法,从不同角度对AAU的发病机制进行研究,为此开展了两项研究工作:一方面利用GEO数据,从探讨疾病的共同性因素和特异性因素角度,分析HLA-B27相关性疾病的关键基因与潜在作用机制;另一方面利用GWAS数据和孟德尔随机化,从遗传学的角度,探讨了免疫细胞与前葡萄膜炎的关联性。第一部分基于GEO数据库探讨HLA-B27相关疾病的关键基因与潜在作用机制目的利用GEO数据库,通过生物信息学方法,探讨AAU、溃疡性结肠炎(Ulcerative colitis,UC)、克罗恩病(Crohn’s disease,CD)、中轴型 SpA (axial spondyloarthritis,axSpA)和PsA为代表的HLA-B27相关疾病的关键基因及潜在机制。方法1.从GEO数据库中,分别下载AAU、UC、CD、axSpA以及PsA的原始表达谱数据,经过标准化预处理后,应用R包筛选出差异表达基因(differentially expressed genes,DEGs) 。2.对五种疾病DEGs采用韦恩分析,从三个角度分别进行分析:仅与单一疾病(AAU、UC、CD、axSpA或PsA)相关的DEGs、仅与关节炎相关疾病(axSpA、PsA)或关节外炎症性疾病(AAU、UC、CD)相关的DEGs,以及与关节炎相关疾病及关节外炎症性疾病均相关的DEGs。3.仅与单一疾病相关的 DEGs 行 LASSO 回归 (least absolute shrinkage and selection operator regression,最小绝对收缩和选择算子回归)筛选疾病“特征基因”,并通过受试者工作(receiver operating characteristic,ROC)曲线评估确定“诊断基因”。构建包含诊断基因的临床预测模型列线图,使用校准曲线及决策曲线分析评估列线图的临床应用价值。4.构建仅与关节炎相关疾病/关节外炎症性疾病相关的DEGs的蛋白质-蛋白质相互作用(protein-protein interaction,PPI)网络,通过MCC评分筛选Hub基因,使用Metascape在线数据库对Hub基因富集通路分析。5.基于五种疾病的GEO数据集,采用单样本免疫浸润分析(single sample Gene Set Enrichment Analysis,ssGSEA)量化28种免疫细胞浸润水平,筛选差异免疫细胞亚群。6.整合多源生物信息学数据库,检索Hub基因相关mRNA、miRNA、lncRNA和转录因子(transcription factor,TF),构建竞争性内源 RNA (competing endogenous RNA,ceRNA)和ceRNA-TF调控网络。基于DGIdb数据库进行药理学分析,筛选潜在靶向治疗药物。7.在UC、CD、AAU验证集中验证基因的表达水平。收集UC和AS患者的外周血单个核细胞(peripheral blood mononuclear cells,PBMCs),通过 RT-qPCR验证基因表达水平。结果1.通过差异表达分析,在AAU、UC、CD、axSpA和PsA数据集中分别筛选出481、2682、2232、2485和380个DEGs。韦恩分析显示:仅1个基因(ABCD2)同时与关节炎相关疾病及关节外炎症性疾病均相关;仅与AAU、UC、CD、axSpA和PsA相关的DEGs数量分别为324、591、206、2013和240个;仅与关节外炎症性疾病相关的DEGs数量为74个,仅与关节炎相关疾病共享相关的DEGs数量为60个。2.LASSO回归分析筛选出AAU、UC、CD、axSpA和PsA的特征基因分别为6、17、9、34和10个,ROC曲线分析进一步确定4个AAU、15个UC、9个CD、3个axSpA和6个PsA诊断基因。3.仅与关节外炎症性疾病相关的DEGs的PPI网络分析和MCC评分筛选出评分最高的5个Hub基因,分别为IL6、FN1、F2R、HIF1A和ANGPT2,这些Hub基因主要富集于PI3K-Akt信号通路、焦点粘附:PI3K-Akt-mTOR信号通路。关节炎相关疾病的5个Hub基因分别为LAG3、IL15、PRF1、TBX21和IL2RB5,显著富集于淋巴细胞活化、免疫效应过程和T细胞分化的调控。4.免疫浸润分析表明AAU组的CD8+T细胞、MHC Ⅰ类阳性细胞、滤泡辅助T细胞的得分均呈现出显著下降趋势。在UC患者组与对照组之间,除了细胞溶解活性(cytolytic activity)、树突状细胞、未成熟树突状细胞外,其余免疫相关基因集均存在显著差异。在CD患者组与对照组之间,有22个免疫相关基因集的评分存在显著差异,主要包括成熟树突状细胞、抗原呈递细胞共刺激(APC co stimulation)、趋化因子受体(chemokine receptor,CCR)。在 AxSpa 患者组与对照组之间,研究发现有9个免疫相关基因集存在显著差异,包括APC co stimulation、CCR、CD8阳性T细胞等。在PsA患者组与对照组之间,APC co stimulation和cytolytic activity的得分存在显著差异。5.关节外炎症性疾病的Hub基因ceRNA调控轴主要包括:AZIN1-AS1-’h sa-miR-941’-ANGPT2、SNHG7-’hsa-miR-671-5p’-IL6、DRAIC-’hsa-miR-34 a-5p’-FN1、TERC-’hsa-miR-34a-5p’-HIF1A及 GNAS-AS1-’hsa-miR-24-3p’-F2R,其ceRNA-TF互作网络显示:TWIST2调控F2R和FN1,VHL调控HIF1 A与ANGPT2,FOXO1可能参与IL6和ANGPT2的调控。关节炎相关疾病的Hu b 基因 ceRNA 调控轴主要包括:DANCR-AS1-’hsa-miR-629-5p’-IL2RB、MAL AT1-’hsa-miR-1270’-IL15、HCG9-’hsa-miR-2114-5p’-PRF1以及LINC02432-’hsa-miR-133a-3p’-TBX21,对应的 ceRNA-TF 互作网络显示:SP1 调控IL2 RB和TBX21,STAT4调控PRF1和TBX21。6.药物-基因相互作用分析显示,PRF1、IL15、LAG3、IL2RB、FN1、IL6、HIF1A、ANGPT2以及F2R基因分别与2种、5种、2种、4种、4种、25种、136种、4种和16种药物存在潜在相互作用关系。7.RT-qPCR验证结果显示,ABCD2在AS和UC患者的PBMC中表达显著升高,AS患者中PRF1 mRNA表达水平显著升高,UC患者中IL6、FN1和F2R mRNA表达水平显著升高。结论ABCD2是HLA-B27相关疾病共同的生物标志物,PRF1被鉴定为HLA-B27相关关节炎的共同关键基因,而IL6、FN1和F2R可能通过协同作用促进HLA-B27相关关节外炎症性疾病的发生和发展。不同类型的HLA-B27相关疾病均具有特异性诊断基因,关节炎相关疾病及关节外炎症性疾病组各自的Hub基因与不同的生物学功能相关,为进一步研究影响这类疾病发生的共同性因素和特异性因素提供了科学证据。第二部分基于孟德尔随机化研究免疫细胞与前葡萄膜炎发病风险的因果关系目的前葡萄膜炎是葡萄膜炎最常见的类型,免疫细胞在其发生中发挥重要作用,但是其具体机制尚未完全阐明。本研究旨在利用GWAS数据,通过两样本的孟德尔随机化(mendelian randomization,MR)的研究方法,探究免疫细胞与前葡萄膜炎的因果关系。方法以GWAS Catalog数据库的731种免疫细胞表型作为暴露,来自芬兰数据库(FinnGen)的虹膜睫状体炎(属前葡萄膜炎,本研究中仍使用原数据库采用的命名方法)数据作为结局,采用逆方差加权(Inverse-variance weighted,IVW)法评估731种免疫细胞与虹膜睫状体炎(iridocyclitis,IR)的因果关联,同时结合MR-Egger回归、加权中位数法、简单模式法及加权模式法进行多重验证,以增强结果的稳健性。通过Cochran’s Q检验、MR-Egger回归等进行敏感性分析。此外,以IR作为暴露因素,以潜在免疫细胞特征作为结局变量,通过反向MR分析探究免疫细胞与IR之间的因果关联。结果MR分析发现6种与IR相关的免疫细胞表型,7种与急性和亚急性虹膜睫状体炎(acute and subacute iridocyclitis,ASIR)相关的免疫细胞表型,5种与慢性虹膜睫状体炎(chronic iridocyclitis,CIR)相关的免疫细胞表型。在ASIR相关的 7 种免疫细胞表型中,CD8 on TD CD8br (OR=1.891,95% CI:1.471-2.432,p=6.72E-07)和 CD33-HLADR+(OR=1.224,95% CI:1.177-1.273,p=7.13E-24)增加 ASIR 的发病风险,CD4 on HLADR+CD4+(OR=0.256,95% CI:0.162-0.403,p=4.32E-09)、CD33dim HLA DR+CD11b+(OR=0.717,95% CI:0.653-0.786,p=1.86E-12)、CD33dim HLA DR+CD11b-(OR=0.721,95% CI:0.658-0.790,p=1.94E-12)、 HLA DR on CD14-CD16-(OR=0.481,95% CI:0.365-0.634,P=1.93E-07)以及 HLA-DR on CD14-CD16+monocytes (OR=0.708,95% CI:0.643-0.780,p=1.93E-12)能显著降低ASIR的发病风险。对MR分析的结果进行敏感性分析未检测到工具变量间具有显著异质性或水平多效性证据。反向孟德尔随机化分析显示,IR在免疫细胞表型之间无显著关联。结论本研究从遗传学的角度,初步揭示多种免疫细胞与前葡萄膜炎存在强因果关系,CD8 on TD CD8br细胞和HLA DR on CD33-HLA DR+的骨髓来源的抑制性细胞(myeloid-derived suppressor cells,MDSCs)可能是促进 ASIR 发生的潜在危险因素,而 CD4 on HLA DR+CD4+T 细胞、CD33dim HLA DR+CD11b+及CD33dim HLADR+CD11b-的 MDSCs 亚群,以及 HLADR on CD14-CD16+单核细胞与ASIR呈负向遗传相关性,可能对ASIR产生一定保护作用。更多还原

【Abstract】 Acute anterior uveitis(AAU)is closely associated with human leukocyte antigen(HLA)-B27,and it may occur with or without systemic diseases.The systemic diseases associated with AAU are mainly seronegative spondyloarthritis(SpA),represented by ankylosing spondylitis(AS),inflammatory bowel disease(IBD,including ulcerative colitis and Crohn’s disease),and psoriatic arthritis(PsA).These systemic diseases themselves are closely related to HLA-B27.It can be considered that HLA-B27 is the common genetic background of such diseases.However,non-HLA-B27 genetic factors,immune factors,and environmental factors also play important roles in the occurrence of these diseases.In recent years,the rapid development of bioinformatics technologies and the continuous update of various omics databases,such as the Gene Expression Omnibus(GEO)and genome-wide association study(GWAS)databases,have provided possibilities for researchers to carry out joint and multi-omics analyses of AAU and HLA-B27-related diseases.We hope to use bioinformatics methods to study the pathogenesis of AAU from different perspectives.To this end,we have carried out two research works:On the one hand,GEO data were used to analyze the key genes and potential mechanisms of HLA-B27-related diseases from the perspective of exploring common and specific factors of diseases;on the other hand,GWAS data and Mendelian randomization were used to explore the association between immune cells and anterior uveitis from a genetic perspective.Part Ⅰ:Exploration of Key Genes and Potential Mechanisms in HLA-B27 related Diseases Based on GEO DatabaseObjectiveTo investigate the key genes and potential mechanisms underlying HLA-B27 related diseases,including acute anterior uveitis(AAU),ulcerative colitis(UC),Crohn’s disease(CD),axial spondyloarthritis(axSpA),and psoriatic arthritis(PsA),using bioinformatics methods based on GEO database.Methods1.Download the raw expression profile data of AAU,UC,CD,axSpA,and PsA from the GEO database,respectively.After standardized pretreatment,use R packages to screen differentially expressed genes(DEGs).2.Perform Venn analysis on the DEGs of the five diseases from three perspectives:DEGs associated with a single disease(AAU,UC,CD,axSpA,or PsA)only;DEGs associated with arthritis-related diseases(axSpA,PsA)or extra-articular inflammatory diseases(AAU,UC,CD)only;and DEGs associated with both arthritis-related and extra-articular inflammatory diseases.3.For DEGs associated with a single disease only,perform LASSO(least absolute shrinkage and selection operator)regression to screen "feature genes" of the diseases,and determine "diagnostic genes" by evaluating receiver operating characteristic(ROC)curves.Construct a clinical prediction nomogram containing diagnostic genes,and assess the clinical application value of the nomogram using calibration curves and decision curve analysis.4.Construct a protein-protein interaction(PPI)network for DEGs associated with arthritis-related diseases/extra-articular inflammatory diseases only.Screen hub genes via MCC scoring,and perform pathway enrichment analysis of hub genes using the Metascape online database.5.Based on the GEO datasets of the five diseases,quantify the infiltration levels of 28 immune cell subsets using single-sample Gene Set Enrichment Analysis(ssGSEA),and screen differentially infiltrated immune cell subsets.6.Integrate multi-source bioinformatics databases to retrieve mRNA,miRNA,IncRNA,and transcription factors(TFs)related to hub genes,and construct competing endogenous RNA(ceRNA)and ceRNA-TF regulatory networks.Perform pharmacological analysis based on the DGIdb database to screen potential targeted therapeutic drugs.7.Validate the gene expression levels in the UC,CD,and AAU validation sets.Collect peripheral blood mononuclear cells(PBMCs)from UC and AS patients,and verify gene expression levels by RT-qPCR.Results1.Through differential expression analysis,481,2682,2232,2485,and 380 DEGs were screened in AAU,UC,CD,axSpA,and PsA datasets,respectively.Venn analysis showed:only 1 gene(ABCD2)was simultaneously associated with both arthritis-related diseases and extra-articular inflammatory diseases;the numbers of DEGs associated with AAU,UC,CD,axSpA,and PsA alone were 324,591,206,2013,and 240,respectively;the number of DEGs associated with extra-articular inflammatory diseases alone was 74,and the number of DEGs shared by arthritis-related diseases alone was 60.2.LASSO regression analysis screened 6,17,9,34,and 10 feature genes for AAU,UC,CD,axSpA,and PsA,respectively.ROC curve analysis further identified 4 AAU,15 UC,9 CD,3 axSpA,and 6 PsA diagnostic genes.3.PPI network analysis and MCC scoring of DEGs associated with extra-articular inflammatory diseases alone screened the top 5 Hub genes:IL6,FN1,F2R,HIF1A,and ANGPT2.These Hub genes were mainly enriched in the PI3K-Akt signaling pathway and focal adhesion:PI3K-Akt-mTOR signaling pathway.The 5 Hub genes for arthritis-related diseases were LAG3,IL15,PRF1,TBX21,and IL2RB5,which were significantly enriched in the regulation of lymphocyte activation,immune effector processes,and T cell differentiation.4.Immune infiltration analysis showed that the scores of CD8+T cells,MHC class I-positive cells,and follicular helper T cells in the AAU group all showed a significant downward trend.Between the UC patient group and the control group,significant differences existed in all immune-related gene sets except for cytolytic activity,dendritic cells,and immature dendritic cells.Between the CD patient group and the control group,22 immune-related gene sets showed significant differences in scores,mainly including mature dendritic cells,antigen-presenting cell co-stimulation(APC co-stimulation),and chemokine receptor(CCR).Between the AxSpa patient group and the control group,9 immune-related gene sets were found to be significantly different,including APC co-stimulation,CCR,CD8-positive T cells,etc.Between the PsA patient group and the control group,the scores of APC co-stimulation and cytolytic activity showed significant differences.5.The ceRNA regulatory axes of Hub genes in extra-articular inflammatory diseases mainly included AZIN1-AS1-’hsa-miR-941’-ANGPT2,SNHG7-’hsa-miR-671-5p’-IL6,DRAIC-’hsa-miR-34a-5p’-FN1,TERC-’hsa-miR-34a-5p’-HIF1A,and GNAS-AS1-’hsa-miR-24-3p’-F2R.The ceRNA-TF interaction network showed that TWIST2 regulates F2R and FN1,VHL regulates HIF1A and ANGPT2,and FOXO1 may be involved in the regulation of IL6 and ANGPT2.The ceR NA regulatory axes of Hub genes in arthritis-related diseases mainly included DANCR-AS1-’hsa-miR-629-5p’-IL2RB,MALAT1-’hsa-miR-1270’-IL15,HCG9-’hs a-miR-2114-5p’-PRF1,and LINC02432-’hsa-miR-133a-3p’-TBX21.The corresponding ceRNA-TF interaction network showed that SP1 regulates IL2RB and TB X21,and STAT4 regulates PRF1 and TBX21.6.Drug-gene interaction analysis showed that PRF1,IL15,LAG3,IL2RB,FN1,IL6,HIF1A,ANGPT2,and F2R genes had potential interaction relationships with 2,5,2,4,4,25,136,4,and 16 drugs,respectively.7.RT-qPCR validation results showed that ABCD2 was significantly upregulated in PBMCs of AS and UC patients.PRF1 mRNA expression was significantly increased in AS patients,and IL6,FN1,and F2R mRNA expressions were significantly increased in UC patients.ConclusionABCD2 was identified as a common biomarker across HLA-B27 related diseases,while PRF1 was established as a key shared gene specifically associated with HLA-B27 related arthropathies.Notably,IL6,FN1 and F2R appear to synergistically contribute to the pathogenesis of HLA-B27-associated extra-articular inflammatory diseases.Importantly,distinct diagnostic genes were identified for each subtype of HLA-B27 related disorders.The hub genes in arthritis-related versus extra-articular inflammatory disease groups were functionally linked to divergent biological pathways,providing compelling scientific evidence for investigating both shared and disease-specific pathogenic mechanisms in this spectrum of conditions.Part Ⅱ:Investigating the Causal Relationship Between Immune Cells and Anterior Uveitis Using Mendelian randomizationObjectiveAnterior uveitis is the most common type of uveitis,and immune cells play an important role in its occurrence,but the specific mechanism has not been fully elucidated.This study aims to use GWAS data and a two-sample Mendelian randomization(MR)approach to explore the causal relationship between immune cells and anterior uveitis.MethodsUsing 731 immune cell phenotypes from the GWAS Catalog database as exposures and data on iridocyclitis(a type of anterior uveitis,using the original database nomenclature in this study)from the Finnish FinnGen database as the outcome,the inverse-variance weighted(IVW)method was used to evaluate the causal association between 731 immune cells and iridocyclitis(IR).Multiple validations were performed using MR-Egger regression,weighted median method,simple mode method,and weighted mode method to enhance the robustness of the results.Sensitivity analyses,including Cochran’s Q test and MR-Egger regression,were conducted to detect heterogeneity or horizontal pleiotropy.Additionally,reverse MR analysis was performed with iridocyclitis as the exposure and potential immune cell traits as the outcomes to explore the causal association between immune cells and IR.ResultsMR analysis identified 6 immune cell phenotypes associated with IR,7 associated with acute and subacute iridocyclitis(ASIR),and 5 associated with chronic iridocyclitis(CIR).Among the 7 ASIR-related immune cell phenotypes,CD8 on TD CD8br(OR=1.891,95%CI:1.471-2.432,p=6.72E-07)and CD33-HLA DR+(OR=1.224,95%CI:1.177-1.273,p=7.13E-24)increased the risk of ASIR,while CD4 on HLA DR+CD4+(OR=0.256,95%CI:0.162-0.403,p=4.32E-09),CD33dim HLA DR+ CD11b+(OR=0.717,95%CI:0.653-0.786,p=1.86E-12),CD33dim HLA DR+CD11b-(OR=0.721,95%CI:0.658-0.790,p=1.94E-12),HLA DR on CD14CD16-(OR=0.481,95%CI:0.365-0.634,p=1.93E-07),and HLA DR on CD14CD16+monocytes(OR=0.708,95%CI:0.643-0.780,p=1.93E-12)significantly reduced the risk of ASIR.Sensitivity analyses detected no significant heterogeneity among instrumental variables or evidence of horizontal pleiotropy.Reverse Mendelian randomization analysis showed no significant association between IR and immune cell phenotypes.ConclusionThis study,from a genetic perspective,preliminarily reveals a strong causal relationship between multiple immune cells and anterior uveitis.CD8 on TD CD8br cells and HLA DR on CD33-HLA DR+myeloid-derived suppressor cells(MDSCs)may serve as potential risk factors promoting the occurrence of ASIR.In contrast,CD4 on HLA DR+CD4+T cells,CD33dim HLA DR+CD11b+and CD33dim HLA DR+CD11b-MDSC subsets,as well as HLA DR on CD14-CD16+ monocytes,exhibit negative genetic correlation with ASIR,potentially exerting protective effects against ASIR.更多还原