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Rheumatoid arthritis-associated gene-gene interaction network for rheumatoid arthritis candidate genes.

ABSTRACT: Rheumatoid arthritis (RA, MIM 180300) is a chronic and complex autoimmune disease. Using the North American Rheumatoid Arthritis Consortium (NARAC) data set provided in Genetic Analysis Workshop 16 (GAW16), we used the genotype-trait distortion (GTD) scores and proposed analysis procedures to capture the gene-gene interaction effects of multiple susceptibility gene regions on RA. In this paper, we focused on 27 RA candidate gene regions (531 SNPs) based on a literature search. Statistical significance was evaluated using 1000 permutations. HLADRB1 was found to have strong marginal association with RA. We identified 14 significant interactions (p < 0.01), which were aggregated into an association network among 12 selected candidate genes PADI4, FCGR3, TNFRSF1B, ITGAV, BTLA, SLC22A4, IL3, VEGF, TNF, NFKBIL1, TRAF1-C5, and MIF. Based on our and other contributors' findings during the GAW16 conference, we further studied 24 candidate regions with 336 SNPs. We found 23 significant interactions (p-value < 0.01), nine interactions in addition to our initial findings, and the association network was extended to include candidate genes HLA-A, HLA-B, HLA-C, CTLA4, and IL6. As we will discuss in this paper, the reported possible interactions between genes may suggest potential biological activities of RA.

SUBMITTER: Huang CH

PROVIDER: S-EPMC2795977 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

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Rheumatoid arthritis-associated gene-gene interaction network for rheumatoid arthritis candidate genes.

Huang Chien-Hsun CH Cong Lei L Xie Jun J Qiao Bo B Lo Shaw-Hwa SH Zheng Tian T

BMC proceedings 20091215

Rheumatoid arthritis (RA, MIM 180300) is a chronic and complex autoimmune disease. Using the North American Rheumatoid Arthritis Consortium (NARAC) data set provided in Genetic Analysis Workshop 16 (GAW16), we used the genotype-trait distortion (GTD) scores and proposed analysis procedures to capture the gene-gene interaction effects of multiple susceptibility gene regions on RA. In this paper, we focused on 27 RA candidate gene regions (531 SNPs) based on a literature search. Statistical signif ...[more]

PMID: 20018070

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Project description:Rheumatoid arthritis (RA), which normally manifests as a multi‑joint inflammatory reaction, is a common immunological disease in clinical practice. However, the pathogenesis of RA has not yet been fully elucidated. Rituximab (RTX) is an effective drug in the treatment of RA, however its therapeutic efficacy and mechanism of action require further investigation. Thus, the present study aimed to screen the candidate key regulatory genes and explain the potential mechanisms of RA. Gene chips of RA and normal joint tissues were analyzed and, gene chips of RTX before and after treatment were investigated. In the present study, strong evidence supporting the pathogenesis of RA and mechanism of action of RTX were also revealed. Differentially expressed genes (DEGs) were analyzed using the limma package of RStudio software. A total of 1,150 DEGs were detected in RA compared with normal joint tissues. The upregulated genes were enriched in 'interleukin‑12 production', 'I‑κB kinase/NF‑κB signaling', 'regulation of cytokine production involved in immune response' and 'cytokine metabolic process'. Functional enrichment analysis showed that RTX was primarily involved in the inhibition of 'adaptive immune response', 'B cell activation involved in immune response' and 'immune effector process'. Subsequently, leukocyte immunoglobulin‑like receptor subfamily B member 1 (LILRB1), a hub gene with high connectivity degree, was selected, and traditional Chinese medicine libraries were molecularly screened according to the structure of the LILRB1 protein. The results indicated that kaempferol 3‑O‑β‑D‑glucosyl‑(1→2)‑β‑D‑glucoside exhibited the highest docking score. In the present study, the DEGs and their biological functions in RA and the pharmacological mechanism of RTX action were determined. Taken together, the results suggested that LILRB1 may be used as a molecular target for RA treatment, and kaempferol 3‑O‑β‑D‑glucosyl‑(1→2)‑β‑D‑glucoside may inhibit the pathological process of RA.

Project description:Genotyping was performed using a custom-designed Illumina 384-SNP VeraCode microarray (Illumina) to determine possible associations of genes encoding PADI1, PADI2, PADI3, PADI4, PADI6, PRKD3, Gc, GLRX,CDSN, PSORS1C1, TXNDC5, CA1, bub3, SORBS1, VDR,SERPNA1, PCSK6, DNAH9 to rheumatoid arthrities (RA) and ankylosing spondylitis (AS). We previously found that encoding genes of PADI1, PADI2, PADI3, PADI4, PADI6, PRKD3, Gc, GLRX,CDSN, PSORS1C1, TXNDC5, CA1, bub3, SORBS1, VDR, SERPNA1, PCSK6 and DNAH9 could be associated with rheumatoid arthritis (RA) and ankylosing spondylitis (AS). We used illumina customer designed microarray to verify the above finding with an increased numbers of samples. Genotyping was performed using a custom-designed Illumina 384-SNP VeraCode microarray (Illumina) to determine possible associations of genes encoding PADI1, PADI2, PADI3, PADI4, PADI6, PRKD3, Gc, GLRX,CDSN, PSORS1C1, TXNDC5, CA1, bub3, SORBS1, VDR, SERPNA1, PCSK6, DNAH9 to RA and AS. A case-control study was performed with 51 AS patients, 266 rheumatoid arthritis (RA) and 163 health controls. Tag single nucleotide polymorphisms (tag SNPs) across the above genes were determined by searching the HapMap database. The tag SNPs were selected on the basis of linkage disequilibrium patterns observed in the Han Chinese in Beijing (CHB) samples that were genotyped as a part of the International HapMap Project. Only SNPs with a minor allele frequency of greater than 5% with a pair-wise r2 ≥ 0.8 in the HapMap were considered. SNPs that were in exons and untranslated regions (UTR), as well as promoters and introns within 500 bp of exons were also selected. Candidate SNPs were submitted to Illumina for a design score. The Illumina Assay Design Tool filtered out SNPs that were not suitable for the Illumina platform. Finally, 382 SNPs with a design score of “1” were selected. 382 SNPs within PADI1, PADI2, PADI3, PADI4, PADI6, PRKD3, Gc, GLRX,CDSN, PSORS1C1, TXNDC5, CA1, bub3, SORBS1, VDR,SERPNA1, PCSK6, DNAH9 encoding genes were selected. Genotyping was performed using Illumina VeraCode microarray in a case-control study including 51 AS patients, 266 rheumatoid arthritis (RA) and 163 health controls We performed genotyping using a custom-designed Illumina 384-SNP VeraCode microarray (Illumina). Peripheral blood samples were collected from patients with RA (n=266, 183 female) and AS (n=51, 10 female). RA patients had a mean age of 51.7 years, while AS patients had a mean age of 35.9 years. A total of 163 (60 female) healthy individuals with a mean age of 48.0 years were blood donors. Blood samples were put into Monovette tubes containing 3.8% sodium citrate. The genotyping was conducted with the BeadXpress Reader using the Illumina VeraCode GoldenGate Assay kit. 500 ng of sample DNA were used per assay. Genotype clustering and calling were performed using BeadStudio software (Genomestudio V2010.2 Genotyping module V1.6) (Illumina). This work was completed at the Beijing Institute of Genomics.

Project description:ObjectiveRheumatoid arthritis (RA) is one of the most prevalent inflammatory arthritis worldwide. However, the genes and pathways associated with macrophages from synovial fluids in RA patients still remain unclear. This study aims to screen and verify differentially expressed genes (DEGs) related to identifying candidate genes related to synovial macrophages in rheumatoid arthritis by bioinformatics analysis.MethodsWe searched the Gene Expression Omnibus (GEO) database, and GSE97779 and GSE10500 with synovial macrophages expression profiling from multiple RA microarray dataset were selected to conduct a systematic analysis. GSE97779 included nine macrophage samples from synovial fluids of RA patients and five macrophage samples from primary human blood of HC. GSE10500 included five macrophage samples from synovial fluids of RA patients and three macrophage samples from primary human blood of HC. Functional annotation of DEGs was performed, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-protein interaction (PPI) network of DEGs was established using the STRING database. CytoHubba was used to identify hub genes. MCODE was used to determine gene clusters in the interactive network.ResultsThere were 2638 DEGs (1425 upregulated genes and 1213 downregulated ones) and 889 DEGs (438 upregulated genes and 451 downregulated ones) selected from GSE97779 and GSE10500, respectively. Venn diagrams showed that 173 genes were upregulated and 106 downregulated in both two datasets. The top 10 hub genes, including FN1, VEGFA, HGF, SERPINA1, MMP9, PPBP, CD44, FPR2, IGF1, and ITGAM, were identified using the PPI network.ConclusionThis study provides new insights for the potential biomarkers and the relevant molecular mechanisms in RA patients. Our findings suggest that the 10 candidate genes might be used in diagnosis, prognosis, and therapy of RA in the future. However, further studies are required to confirm the expression of these genes in synovial macrophages in RA and control specimen.

Dataset Information

Rheumatoid arthritis-associated gene-gene interaction network for rheumatoid arthritis candidate genes.

Publications

Rheumatoid arthritis-associated gene-gene interaction network for rheumatoid arthritis candidate genes.

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