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Evaluating gene x gene and gene x smoking interaction in rheumatoid arthritis using candidate genes in GAW15.

ABSTRACT: We examined the potential gene x gene interactions and gene x smoking interactions in rheumatoid arthritis (RA) using the candidate gene data sets provided by Genetic Analysis Workshop 15 Problem 2. The multifactor dimensionality reduction (MDR) method was used to test gene x gene interactions among candidate genes. The case-only sample was used to test gene x smoking interactions. The best predictive model was the single-locus model with single-nucleotide polymorphism (SNP) rs2476601 in gene PTPN22. However, no clear gene x gene interaction was identified. Substantial departure from multiplicativity was observed between smoking and SNPs in genes CTLA4, PADI4, MIF, and SNPs on chromosome 5 and one haplotype of PTPN22. The strongest evidence of association was identified between the PTPN22 gene and RA status, which was consistently detected in single SNP association, gene x gene interaction and gene x smoking interaction analyses.

SUBMITTER: Mei L

PROVIDER: S-EPMC2367546 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Evaluating gene x gene and gene x smoking interaction in rheumatoid arthritis using candidate genes in GAW15.

Mei Ling L Li Xiaohui X Yang Kai K Cui Jinrui J Fang Belle B Guo Xiuqing X Rotter Jerome I JI

BMC proceedings 20071218

We examined the potential gene x gene interactions and gene x smoking interactions in rheumatoid arthritis (RA) using the candidate gene data sets provided by Genetic Analysis Workshop 15 Problem 2. The multifactor dimensionality reduction (MDR) method was used to test gene x gene interactions among candidate genes. The case-only sample was used to test gene x smoking interactions. The best predictive model was the single-locus model with single-nucleotide polymorphism (SNP) rs2476601 in gene PT ...[more]

PMID: 18466513

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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: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

Evaluating gene x gene and gene x smoking interaction in rheumatoid arthritis using candidate genes in GAW15.

Publications

Evaluating gene x gene and gene x smoking interaction in rheumatoid arthritis using candidate genes in GAW15.

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