| BackgroundRheumatic diseases are painful conditions usually characterized by inflammation, swelling, and pain in joints or muscles. The underlying mechanisms of most rheumatic diseases are still poorly understood. The shared symptoms across different rheumatic diseases suggested that there may exist common mechanisms underlying these diseases. Extensive gene expression studies, accumulated thus far, have identified signature molecules for each rheumatic disease, and also found factors shared by multiple diseases. However, studies which particularly focused on the common genetic mechanisms of rheumatic diseases were seldom, and diseases covered in such studies were limited. Common factors shared by various rheumatic diseases have yet to be studied systematically.Objective1. To identify common genetic factors of rheumatic diseases in human peripheral blood mononuclear cells(PBMCs), and to further explore the interactions and biological functions of the identified factors.2. To test the identified genes in a new sample of rheumatic diseases patients and comparable individuals without any kind of rheumatic diseases. To represent the performance of the tested genes in distinguishing rheumatic patients and normal controls.3. To identify epigenetic factors that could regulate the expression level of the tested genes.Materials and Methods1. We searched the NCBI Pubmed and Gene Exession Ominibus(GEO) database for gene expression datasets related to 23 types of common rheumatic diseases.2. Processing methods for gene expression datasets from GEO database: we processed the datasets by using an R package: Meta DE. The t-test was selected for significance analysis. The Benjamini & Hochberg FDR method was used to apply p-value adjustment for multiple-testing correction. The protein-protein interaction and gene ontology enrichment analysis of the identified genes were performed by using STRING 9.1 and Ami Go separately.3. A total of twenty seven rheumatoid arthritis(RA) patients and eighteen comparable individuals without any types of rheumatic diseases were included in this study. RA patients were recruited from the first affiliated Hospital of Soochow University(Suzhou, China). Controls were recruited from the Soochow University(Suzhou, China). All subjects signed informed consent documents before entering the project and each donated 10 ml peripheral blood.4. The demographic characteristics, lifestyle, health condition of all recruited samples and the diseases related information of RA patients were collected by trained investigators. PBMCs were isolated from the blood samples using Lymphoprep solution. m RNA, mi RNA and DNA methylation were profiled using the Agilent Lnc RNA & m RNA microarray chip, the Affymetrix mi RNA 4.0 chip and the Illumina human 450 K chip separately.5. SPSS 21 was used for statistical analysis. Two-tailed student t-test was adopted to perform gene differential expression analysis. The receiver operating characteristic curve(ROC) was used to represent the performance of the tested genes in distinguishing rheumatic patients and normal controls. The Medcalc software was used to draw the ROC curve and make comparisons of the area under the ROC curve(AUC) between genes. Novel functional mi RNAs of tested genes were derived from Tar Base and Target Scan. The coefficient of Pearson’s correlation was adopted to assess the mi RNA-m RNA and m RNA-DNA methylation correlation. The correlation network of the mi RNA, m RNA and DNA methylation was visualized using Cytoscape.Results1. A total of 6 GEO datasets were included in this study, covering 4 types of rheumatic diseases(2, 2, 1, 1, datasets on RA, systemic lupus erythematosus(SLE), osteoarthritis(OA), and ankylosing spondylitis(AS), respectively). In addition, we recruited 27 active female RA patients and 18 comparable female individuals without any types of rheumatic diseases. Mean age of RA patients was 47.33±10.91 years old. Mean age of control individuals was 47.11±14.09 years old. The age difference between the two groups was not statistically significant(t=-0.06, P=0.953).2. We identified a total of eight differentially expressed genes(TNFSF10、CX3CR1、LY96、TLR5、TXN、TIA1、PRKCH、PRF1), each associated with at least 3 of the 4 studied rheumatic diseases.3. Protein-protein interaction analysis of the eight identified genes indicated that each gene had text mining associations with others. In addition, there were co-expression relations between three pairs of genes: CX3CR1/PF1、TLR5/TNFSF10 and TNFSF10/PRKCH. Four genes, i.e., CX3CR1, PRF1, TNFSF10, and TLR5, were the nodes of the network.4. Gene ontology enrichment analysis of the identified genes showed that the eight genes were enriched in immune related biological processes, such as cellular defense response, response to biotic stimulus and response to bacterium.5. The expression level of TNFSF10, TXN, TLR5 and TIA1 in RA patients’ s PBMCs were higher than that of non-rheumatic controls(P<0.05). The expression level of LY96、PRKCH、CX3CR1 and PRF1 showed no significant differences between the two groups(P>0.05).6. AUC of the four tested genes(TNFSF10, TXN, TLR5 and TIA1) were 0.882(95%CI:0.750-0.959), 0.835(95%CI:0.695-0.929), 0.817(95%CI:0.673-0.916) and 0.689(95%CI:0.534-0.819), respectively. The AUC of a model combined the four genes were 0.909(95%CI:0.786-0.974). The difference between the largest(TNFSF10: 0.882) and the smallest AUC(TIA1: 0.689) were statistically significant(P<0.05). The AUC of the model combined the four genes was larger than any of the genes, but just statistically larger than TIA1(P<0.05).7. For TNFSF10, TXN, TLR5 and TIA1, we identified 56 pairs of negative correlated mi RNA-m RNA in total. The number of related mi RNAs for TNFSF10, TXN, TLR5 and TIA1 were 7, 2, 17 and 31, respectively. Several mi RNAs regulate more than one target in the network. Hsa-mi R-4443, and hsa-mi R-142-5p regulate TIA1, TLR5 and TNFSF10. Has-mi R-3609 regulates TIA1, TXN and TLR5. Hsa-mi R-342-3p and hsa-mi R-3185 regulate TIA1 and TLR5.8. The expression level of TLR5 was positively correlated with a DNA methylation site(cg17938489) which lies in its 5’UTR. The expression level of TNFSF10 was negatively correlated with DNA methylation site cg1059398 and cg22572614.Conclusion1. The findings support that there exist common factors underlying rheumatic diseases. For RA, SLE, AS and OA, those common factors include TNFSF10, CX3CR1, LY96, TLR5, TXN, TIA1, PRKCH, and PRF1. These genes interact with each other to exert functions related to immune related biological processes.2. We verified 4 of the 8 identified genes in PBMCs of RA patients. The four genes were moderate in distinguishing RA patients and normal controls.3. The four tested genes and corresponding 48 negative correlated mi RNA and 3 DNA methylation site may play roles in the mechanism of RA.4. The eight identified genes in this study are needed to be tested in studies with larger sample size and more types of rheumatic diseases. In-depth studies on these common factors may provide keys to understanding the pathogenesis and developing intervention strategies for rheumatic diseases. |
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