Differential Gene Expression in Synovium of Rheumatoid Arthritis and Osteoarthritis

Rheumatoid arthritis (RA) and osteoarthritis (OA) are the major types of arthritis. Although both diseases are characterized by joint destruction, their etiologies are different. To get insights into pathophysiological pathways, we used the suppression subtractive hybridization (SSH) method to identify differentially expressed genes in RA. DNA sequencing identified 12 gene products including cytoskeletal γ-actin and extracellular matrix components such as fibronectin, collagen IIIα₁, and superficial zone protein. Interferon γ-inducible genes such as a novel thiol reductase, two genes of unknown function (HSIFNIN4, RING3), and annexin II were also found. Two genes encoded proteins involved in proliferation such as elongation factor 1α and the granulin precursor. Furthermore, the protease cathepsin B and synovial phospholipase A2 group IIA were detected by SSH. To confirm the differential expression of the genes, we performed RT-PCR analyses of RA and OA synovial tissues. Compared to OA patients, 9 of the 12 genes were overexpressed in RA, suggesting that SSH is a powerful tool for the detection of differential gene expression in synovial tissues. Further characterization of the gene products may help to identify pathophysiological mechanisms in arthritic diseases.     

Identification of differentially expressed genes in synovial tissue of rheumatoid arthritis and osteoarthritis in patients

Rheumatoid arthritis (RA) and osteoarthritis (OA) are the common joints disorder in the world. Although they have showed the analogous clinical manifestation and overlapping cellular and molecular foundation, the pathogenesis of RA and OA were different. The pathophysiologic mechanisms of arthritis in RA and OA have not been investigated thoroughly. Thus, the aim of study is to identify the potential crucial genes and pathways associated with RA and OA and further analyze the molecular mechanisms implicated in genesis. First, we compared gene expression profiles in synovial tissue between RA and OA from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Gene Expression Series (GSE) 1919, GSE55235, and GSE36700 were downloaded from the GEO database, including 20 patients of OA and 21 patients of RA. Differentially expressed genes (DEGs) including “CXCL13,” “CD247,” “CCL5,” “GZMB,” “IGKC,” “IL7R,” “UBD///GABBR1,” “ADAMDEC1,” “BTC,” “AIM2,” “SHANK2,” “CCL18,” “LAMP3,” “CR1,” and “IL32.” Second, Gene Ontology analyses revealed that DEGs were significantly enriched in integral component of extracellular space, extracellular region, and plasma membrane in the molecular function group. Signaling pathway analyses indicated that DEGs had common pathways in chemokine signaling pathway, cytokine-cytokine receptor interaction, and cytosolic DNA-sensing pathway. Third, DEGs showed the complex DEGs protein-protein interaction network with the Coexpression of 83.22%, Shared protein domains of 8.40%, Colocalization of 4.76%, Predicted of 2.87%, and Genetic interactions of 0.75%. In conclusion, the novel DEGs and pathways between RA and OA identified in this study may provide new insight into the underlying molecular mechanisms of RA.     

Large-scale gene expression profiles, differentially represented in osteoarthritic synovium of the knee joint using cDNA microarray technology

Osteoarthritis (OA) is one of the most common age-related chronic disorders of articular cartilage, joints and bone tissue. Diagnosis of OA commonly depends on clinical and radiographic findings. However, changes in cartilage associated with the early stage of OA cannot be detected using radiographs, because significant cartilage degeneration must occur before radiographic findings show alterations of the appearance of cartilage. To identify new biomarkers of OA, we analysed gene expression profiles of synovium from 43 patients with OA, ten patients with rheumatoid arthritis (RA), and eight non-OA/non-RA patients using a novel cDNA microarray chip. We identified 21 genes with simultaneous significant differences in expression between OA and non-OA/non-RA groups and between OA and RA groups. Linear discriminant analysis showed that the three groups could be well separated using those 21 genes. Statistical analysis also revealed that several of the 21 genes were associated with disease progression and clinical presentation. The graphical modelling method indicated that some of the 21 genes are significantly associated with a particular clinical presentation, suggesting biological relationships among those genes. This is the first report of the use of cDNA microarray technology to create large-scale gene expression profiles differentially expressed in situ in OA synovium of the knee joint.     

Decoding the differential biomarkers of Rheumatoid arthritis and Osteoarthritis: A functional genomics paradigm to design disease specific therapeutics

Rheumatoid arthritis (RA) is a chronic systemic inflammatory disease of unidentified aetiology, chiefly affecting the synovial membranes of joints, cartilage, bone, bursa and tendon sheath. Osteoarthritis (OA) is a degenerative disorder and encompass different sets of pathogenic pathways than RA; however, it shows a medley of clinical manifestations or symptoms resembling RA. Hence, we intend to identify more disease specific biomarkers through the meta-analysis of microarray datasets that can be crucial in the differential diagnosis, disease specific treatment as well as management of both RA and OA in a typical clinical setting.     

Large-scale gene expression profiles,differentially represented in osteoarthritic synovium of the knee joint using cDNA microarray technology

Osteoarthritis (OA) is one of the most common age-related chronic disorders of articular cartilage, joints and bone tissue. Diagnosis of OA commonly depends on clinical and radiographic findings. However, changes in cartilage associated with the early stage of OA cannot be detected using radiographs, because significant cartilage degeneration must occur before radiographic findings show alterations of the appearance of cartilage. To identify new biomarkers of OA, we analysed gene expression profiles of synovium from 43 patients with OA, ten patients with rheumatoid arthritis (RA), and eight non-OA/non-RA patients using a novel cDNA microarray chip. We identified 21 genes with simultaneous significant differences in expression between OA and non-OA/non-RA groups and between OA and RA groups. Linear discriminant analysis showed that the three groups could be well separated using those 21 genes. Statistical analysis also revealed that several of the 21 genes were associated with disease progression and clinical presentation. The graphical modelling method indicated that some of the 21 genes are significantly associated with a particular clinical presentation, suggesting biological relationships among those genes. This is the first report of the use of cDNA microarray technology to create large-scale gene expression profiles differentially expressed in situ in OA synovium of the knee joint.     

Association of MHC and rheumatoid arthritis. Regulatory role of HLA class II molecules in animal models of RA: studies on transgenic/knockout mice

Human leucocyte antigen (HLA) class II molecules have been shown to be associated with predisposition to rheumatoid arthritis (RA). We generated HLA-DR and DQ transgenic mice that lacked endogenous class II molecules to study the interaction between the DR and DQ molecules and define the immunologic mechanisms in rheumatoid arthritis. Using collagen-induced arthritis (CIA) as an experimental model for inflammatory polyarthritis, we show that both DQ and DR are involved in predisposition or resistance to arthritis. Our studies suggest that polymorphism in DQB1 genes may determine predisposition to RA while the DRB1 polymorphism may dictate severity/protection of the disease. These mice provide powerful tools to develop immunotherapeutic protocols.     

Association of MHC and rheumatoid arthritis: Regulatory role of HLA class II molecules in animal models of RA - studies on transgenic/knockout mice

Human leucocyte antigen (HLA) class II molecules have been shown to be associated with predisposition to rheumatoid arthritis (RA). We generated HLA-DR and DQ transgenic mice that lacked endogenous class II molecules to study the interaction between the DR and DQ molecules and define the immunologic mechanisms in rheumatoid arthritis. Using collagen-induced arthritis (CIA) as an experimental model for inflammatory polyarthritis, we show that both DQ and DR are involved in predisposition or resistance to arthritis. Our studies suggest that polymorphism in DQB1 genes may determine predisposition to RA while the DRB1 polymorphism may dictate severity/protection of the disease. These mice provide powerful tools to develop immunotherapeutic protocols.     

Stromal Transcriptional Profiles Reveal Hierarchies of Anatomical Site,Serum Response and Disease and Identify Disease Specific Pathways

Synovial fibroblasts in persistent inflammatory arthritis have been suggested to have parallels with cancer growth and wound healing, both of which involve a stereotypical serum response programme. We tested the hypothesis that a serum response programme can be used to classify diseased tissues, and investigated the serum response programme in fibroblasts from multiple anatomical sites and two diseases. To test our hypothesis we utilized a bioinformatics approach to explore a publicly available microarray dataset including rheumatoid arthritis (RA), osteoarthritis (OA) and normal synovial tissue, then extended those findings in a new microarray dataset representing matched synovial, bone marrow and skin fibroblasts cultured from RA and OA patients undergoing arthroplasty. The classical fibroblast serum response programme discretely classified RA, OA and normal synovial tissues. Analysis of low and high serum treated fibroblast microarray data revealed a hierarchy of control, with anatomical site the most powerful classifier followed by response to serum and then disease. In contrast to skin and bone marrow fibroblasts, exposure of synovial fibroblasts to serum led to convergence of RA and OA expression profiles. Pathway analysis revealed three inter-linked gene networks characterising OA synovial fibroblasts: Cell remodelling through insulin-like growth factors, differentiation and angiogenesis through _3 integrin, and regulation of apoptosis through CD44. We have demonstrated that Fibroblast serum response signatures define disease at the tissue level, and that an OA specific, serum dependent repression of genes involved in cell adhesion, extracellular matrix remodelling and apoptosis is a critical discriminator between cultured OA and RA synovial fibroblasts.     

利用eQTL构建基因-基因网络挖掘类风湿性关节炎风险基因

目的：类风湿性关节炎是一种全身的慢性炎症型疾病,可能影响许多组织和器官,主要发作于灵活的关节。全世界人群中大约有1％会患有类风湿性关节炎。目前已经证实了一些基因与类风湿性关节炎相关,但是这些基因只能解释一小部分遗传风险,因此我们需要新的策略和方法来解决这个问题。方法：表达数量性状位点（eQTL）是指能够调控基因或蛋白质表达的基因组位点,本文采用了eQTL数据构建基因一基因网络并挖掘候选类风湿性关节炎风险基因。结果：首先,利用eQTL数据,基于基因之间的共调控系数,建立基因-基因网络,我们建立了5个不同阈值（0、O．2、0．4、0．6和0．8）的基因-基因网络;然后,在OMIM和GAD数据库中搜索已经证实的与类风湿性关节炎相关的186个基因;最后我们将已证实与类风湿性关节炎相关的186个基因分别投入到这5个网络中,利用基因与基因之间的相关性来挖掘到一些可能与类风湿性关节炎相关的候选风险基因。结论：本文基于eQTL构建了基因．基因网络,结合已知类风湿性关节炎风险基因,挖掘未知风险基因,得到了较好的结果,证明了本方法的有效性,且对于类风湿性关节炎的发病机制研究具有重要价值。除了类风湿性关节炎外,本方法还可推广到其它复杂疾病中,因此本方法对人类复杂疾病的研究具有很强的学术理论价值和应用价值。     

用改良消减杂交筛选类风湿性关节炎滑膜细胞中高表达基因

 为了研究类风湿性关节炎 (rheumatoid arthritis,RA)滑膜细胞 (fibroblast- like synovialcells,FLS)过度增殖和破坏软骨的分子机理 ,利用改良消减杂交法以骨性关节炎 (osteoarthritis,OA)病人滑膜细胞为对照 ,筛选 RA滑膜细胞中的高表达基因 .将得到的基因片段克隆入质粒载体 ,通过反向点杂交排除假阳性克隆后 ,将阳性克隆进行核酸序列分析 ,最后用 Northern杂交方法检测一些高表达基因在 RA和 OA病人滑膜细胞中的表达水平 .结果显示 ,共分离到 1 50个 RA高表达基因片段 ,其中长于 1 0 0 0 bp的片段占 8% (1 2 /1 50 ) ,长于 40 0 bp的片段占 36.7% (55/1 50 ) ,在大于 40 0 bp的片段中 ,假阳性率为 2 3.7% (1 3/55) .在测序的 1 8个片段中 ,已知基因有 1 2个 ,其中包括 IGF- 1结合蛋白 (IGFBP)特异性丝氨酸蛋白酶、层粘连蛋白受体和组织蛋白酶 B等 .新序列有 6个 ,其中两个序列分别与 Ring- box蛋白 1和 SON DNA结合蛋白同源 .对 IGFBP特异性丝氨酸蛋白酶、层粘连蛋白受体和组织蛋白酶 B基因的 Northern杂交分析显示 ,在 RA病人滑膜细胞中 ,这些基因的表达水平高于 OA病人滑膜细胞 .这些结果提示 ,这种改良消减杂交法是一种简便有效的分离差异表达基因的方法 ;IGF- 1结合蛋白特异性丝氨酸蛋白酶、层粘     

Drivers of phenotypic variation in cartilage: Circadian clock genes

Endogenous homeostasis and peripheral tissue metabolism are disrupted by irregular fluctuations in activation, movement, feeding and temperature, which can accelerate negative biological processes and lead to immune reactions, such as rheumatoid arthritis (RA) and osteoarthritis (OA). This review summarizes abnormal phenotypes in articular joint components such as cartilage, bone and the synovium, attributed to the deletion or overexpression of clock genes in cartilage or chondrocytes. Understanding the functional mechanisms of different genes, the differentiation of mouse phenotypes and the prevention of joint ageing and disease will facilitate future research.     

蛋白酪氨酸激酶在类风湿性关节炎滑膜细胞中的表达和功能

克隆类风湿性关节炎（RA）滑膜细胞（FLS）中的蛋白酪氨酸激酶（PTKs）,并研究它们在RA滑膜细胞异常增殖和侵软骨中的作用。根据巳知的PTKs氨基酸序列保守区设计简并引物,采用3'快速末端扩增法（3'RACE)扩增滑膜细胞中PTKs cDNAs的3'末端,将所得序列与Genebank中序列进行比较,以鉴定其是否为巳知的PTKs或与PTKs同源的新序列,然后通过RNA点杂交方法分别观察这些PTKs在RA和骨性关节炎（OA）病人滑膜细胞中的表达水平。结果表明,从RA FLS中克隆到6种巳知PTKs的cDNAs片段,分别为血小板衍生生长因子受体A（PDGFRA）、胰岛素生长因子-1受体（IGF1R）、含discoidin结构域的受体型酪氨酸激酶（DDR2）、Lyn、Janus激酶1（JAK1）和TYK2。RNA点杂交结果显示,在4个RA病人和2个OA病人的滑膜细胞中,PDGFRA、IGF-1R和DDR2在RA滑膜细胞中的表达水平高于OA滑膜细胞,其它几处激酶在两种细胞中的表达水平相同。说明RI滑膜细胞中至少表达PDGFR、IGF1R、Lyn、DDR2、JAK1和TYK2等6种PTKs,其中PDGFRA、IGF1R和DDR2可能与RA滑膜细胞的过度增殖和对软骨的侵蚀性相关。     

Expression of ID family genes in the synovia from patients with rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by aggressive proliferation of synovial tissue leading to destruction of cartilage and bone. To identify molecules which play a crucial role for the pathogenesis, we compared mRNA expression pattern of RA synovium with that of osteoarthritis (OA), using the differential display. From the panel of differentially expressed genes, ID1 (inhibitor of differentiation 1) was considered to be particularly relevant to the pathogenesis of RA, because Id family genes have been shown to play a role in cell proliferation and angiogenesis. To examine whether the up-regulation of these genes is consistently observed in the patients with RA, mRNA levels of ID1 and ID3 in the synovial tissues from 13 patients with RA and 6 patients with OA were semi-quantitatively analyzed by RT-PCR. Mean mRNA levels of ID1 and ID3 were significantly elevated in RA synovia compared with OA by 8.6-fold (P = 0.0044) and 3.3-fold (P = 0.0085), respectively. Immunohistochemistry revealed striking staining of Id1 and Id3 in the endothelial cells, suggesting a possible role of Id in severe angiogenesis observed in RA. The expression of Id family genes in the synovium constitutes a new finding of particular interest. Their functional role as well as their contribution to the genetic susceptibility to RA requires further investigation.     

Genes and environment in arthritis: can RA be prevented?

Understanding of how interactions between genes and environment contribute to the development of arthritis is a central issue in understanding the etiology of rheumatoid arthritis (RA), as well as for eventual subsequent efforts to prevent the disease. In this paper, we review current published data on genes and environment in RA as well as in certain induced animal models of disease, mainly those in which adjuvants only or adjuvants plus organ-specific autoantigens are used to induce arthritis. We refer to some new data on environmental and genetic factors of importance for RA generated from a large case-control study in Sweden (1200 patients, 1200 matched controls). We found an increased risk of seropositive but not of seronegative RA in smokers, and there are indications that this effect may be due to a gene-environment interaction involving MHC class II genes. We also found an increased risk of RA in individuals heavily exposed to mineral oils. This was of particular interest because mineral oils are strong inducers of arthritis in certain rodent strains and because polymorphisms in human genetic regions syntenic with genes predisposing for oil-induced arthritis in rats have now been shown to associate with RA in humans. Taken together, our data support the notion that concepts and data on gene-environment interactions in arthritis can now be taken from induced animal models of arthritis to generate new etiological hypotheses for RA.     

A genome-wide screen of gene–gene interactions for rheumatoid arthritis susceptibility

The objective of the study was to identify interacting genes contributing to rheumatoid arthritis (RA) susceptibility and identify SNPs that discriminate between RA patients who were anti-cyclic citrullinated protein positive and healthy controls. We analyzed two independent cohorts from the North American Rheumatoid Arthritis Consortium. A cohort of 908 RA cases and 1,260 controls was used to discover pairwise interactions among SNPs and to identify a set of single nucleotide polymorphisms (SNPs) that predict RA status, and a second cohort of 952 cases and 1,760 controls was used to validate the findings. After adjusting for HLA-shared epitope alleles, we identified and replicated seven SNP pairs within the HLA class II locus with significant interaction effects. We failed to replicate significant pairwise interactions among non-HLA SNPs. The machine learning approach “random forest” applied to a set of SNPs selected from single-SNP and pairwise interaction tests identified 93 SNPs that distinguish RA cases from controls with 70% accuracy. HLA SNPs provide the most classification information, and inclusion of non-HLA SNPs improved classification. While specific gene–gene interactions are difficult to validate using genome-wide SNP data, a stepwise approach combining association and classification methods identifies candidate interacting SNPs that distinguish RA cases from healthy controls.     

Molecular targets of natural health products in arthritis

Patients with rheumatoid arthritis (RA) and osteoarthritis (OA) consume 'natural health products' (NHPs) whose therapeutic efficacy, toxicity and mechanisms of action are poorly understood. In a previous issue of Arthritis Research and Therapy, Haqqi and colleagues characterized IL-1-activated mitogen-activated protein kinase kinase 3 (MKK3) and p38-mitogen-activated protein kinase (MAPK) isoforms in human OA chondrocytes. The cartilageprotective mechanisms of pomegranate extract involve diminishing MKK3-activated p38α, JNK, NF-κB and Runx2 pathways, which regulate inflammatory proteins and cartilage-destroying proteases. Epigallocatechin- 3-gallate, resveratrol, curcumin and other NHP active ingredients suppress multiple inflammatory and catabolic molecular mediators of arthritis. Non-toxicity, reduced severity and incidence of arthritis in animal models warrant testing NHP active ingredients for preventing human OA and RA.     

Mining functional gene modules linked with rheumatoid arthritis using a SNP-SNP network

The identification of functional gene modules that are derived from integration of information from different types of networks is a powerful strategy for interpreting the etiology of complex diseases such as rheumatoid arthritis (RA). Genetic variants are known to increase the risk of developing RA. Here, a novel method, the construction of a genetic network, was used to mine functional gene modules linked with RA. A polymorphism interaction analy-sis (PIA) algorithm was used to obtain cooperating single nucleotide polymorphisms (SNPs) that contribute to RA disease. The acquired SNP pairs were used to construct a SNP-SNP network. Sub-networks defined by hub SNPs were then extracted and turned into gene modules by mapping SNPs to genes using dbSNP database. We per-formed Gene Ontology (GO) analysis on each gene module, and some GO terms enriched in the gene modules can be used to investigate clustered gene function for better understanding RA pathogenesis. This method was applied to the Genetic Analysis Workshop 15 (GAW 15) RA dataset. The results show that genes involved in func-tional gene modules, such as CD160 (rs744877) and RUNX1 (rs2051179), are especially relevant to RA, which is supported by previous reports. Furthermore, the 43 SNPs involved in the identified gene modules were found to be the best classifiers when used as variables for sample classification.