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Systemic Juvenile Rheumatoid Arthritis and T1D

Systemic Juvenile Rheumatoid Arthritis

Comparative Genomic Analysis of Systemic Juvenile Rheumatoid Arthritis and Type 1 Diabetes Mellitus

In a recent research publication in Scientific Reports, a study was conducted to explore shared genetic markers and immune system involvement in systemic juvenile rheumatoid arthritis (sJRA) and type 1 diabetes mellitus (T1D).


Juvenile idiopathic arthritis (JIA) encompasses a spectrum of chronic rheumatic diseases in children, characterized by diverse genetics, progression patterns, and outcomes.

The classification of JIA continues to evolve due to its heterogeneity. Of particular concern is Systemic Juvenile Rheumatoid Arthritis, which presents severe complications and a range of early symptoms, posing diagnostic challenges.

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T1D is the dominant form of diabetes in pediatric cases, with a rising incidence in younger children and a heightened risk of complications, especially in regions like China, where acute complications such as diabetic ketoacidosis are alarmingly prevalent.

Analyzing gene expressions in peripheral blood mononuclear cells (PBMC), this research establishes a genetic link between JRA and T1D, with a specific focus on the connection between Systemic Juvenile Rheumatoid Arthritis and T1D.

Given the health risks faced by children, further investigation into shared genetic indicators is crucial to enable early diagnosis and intervention for those at high risk of coexisting with these diseases.

Study Overview

Gene expression profiles relevant to JRA and T1D were extracted from the Gene Expression Omnibus (GEO) database using Medical Subject Headings (MeSH) “Arthritis, Juvenile Rheumatoid” and “Diabetes Mellitus Type 1.”

These profiles were filtered based on three criteria: inclusion of Systemic Juvenile Rheumatoid Arthritis, T1D, and control groups; origin from PBMC tissue source; and availability of analytical data.

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A meta-analysis was conducted on datasets GSE7753, GSE21521, GSE193273, and GSE55100 using the R package “ExpressAnalystR.” This process involved individual dataset analysis applying Benjamini-Hochberg’s False Discovery Rate (FDR) with p-values < 0.05.

After merging datasets post-annotation, batch effect adjustments were made to enable unbiased analysis. Fisher’s method was utilized to identify Differentially Expressed Genes (DEGs) with a significant value of <0.05.

To construct the Transcription Factors (TFs)-target SDEGs network, 1665 TFs were sourced from the Human Transcription Factor Database (HumanTFDB).

Using the target database, potential target genes of these TFs within the SDEGs were identified, considering only those with complete evidence. The network was visualized using Cytoscape (3.9.0).

Functional enrichment analysis of gene sets was conducted using various databases, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), Reactome (REAC), and WikiPathways (WP).

Furthermore, the study employed Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), an R package, to assess immune cell infiltration in sJRA and T1D samples.

Specifically, GSE7753 and GSE9006 datasets were used for this analysis due to specific data transformation requirements of CIBERSORT.

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Study Results

The analysis unveiled 245 down-regulated SDEGs and 175 up-regulated SDEGs in sJRA and T1D. These DEGs were predominantly associated with extracellular proteins, with only a small subset (6 up-regulated and 9 down-regulated) unrelated to extracellular proteins.

Using the HumanTFDB as a reference, the comparative analysis identified 13 TFs in the up-regulated SDEGs and a notably higher count of 40 TFs in the down-regulated SDEGs. RUNX3 emerged as the transcription factor with the most potential target genes in the SDEGs.

Interestingly, while RUNX3 was downregulated, both ARID3A and NFE2 showed upregulation. Additionally, ARID3A and NFE2 demonstrated mutual regulation, with ARID3A also regulated by RUNX3.

Functional enrichment of these genes highlighted their involvement in various pathways and processes. Up-regulated SDEGs were associated with 238 GO terms, 62 REAC pathways, and 17 KEGG pathways, playing crucial roles in cell cycle processes, innate immune response regulation, neutrophil degranulation, and signaling pathways, including JAK-STAT and PI3K.

Down-regulated SDEGs were associated with 154 GO terms, 36 REAC pathways, and 17 KEGG pathways, primarily linked to the adaptive immune system, T cell activation, differentiation of various T cell types, cytokine functions, and innate immune system processes related to natural killer cell functions and specific signaling pathways.

Regarding the transcription factors targeting the SDEGs, the pathways they enriched were consistent with those enriched by the up-and-down-regulated SDEGs. Notably, neutrophil degranulation and the cell cycle process were closely linked.

Additionally, terms associated with T cell activation, differentiation, cytokine functions, and natural killer cell activities were commonly enriched between the downregulated SDEGs and the targeted SDEGs.

The analysis extended to assessing the infiltration levels of 22 immune cells in T1D and sJRA samples using the CIBERSORT method. Both sJRA and T1D samples exhibited increased levels of neutrophils and naive Cluster of Differentiation 4 T (CD4 T) cells while displaying decreased CD4 memory resting T cells.

In comparing monocyte infiltration levels between the diseases and control samples, Systemic Juvenile Rheumatoid Arthritis exhibited significantly elevated monocyte levels.