NFIL3 and its immunoregulatory role in rheumatoid arthritis patients

Abstract

Nuclear-factor, interleukin 3 regulated (NFIL3) is an immune regulator that plays an essential role in autoimmune diseases. However, the relationship between rheumatoid arthritis (RA) and NFIL3 remains largely unknown. In this study, we examined NFIL3 expression in RA patients and its potential molecular mechanisms in RA. Increased NFIL3 expression levels were identified in peripheral blood mononuclear cells (PBMCs) from 62 initially diagnosed RA patients and 75 healthy controls (HCs) by quantitative real-time PCR (qRT-PCR). No correlation between NFIL3 and disease activity was observed. In addition, NFIL3 expression was significantly upregulated in RA synovial tissues analyzed in the Gene Expression Omnibus (GEO) dataset (GSE89408). Then, we classified synovial tissues into NFIL3-high (≥75%) and NFIL3-low (≤25%) groups according to NFIL3 expression levels. Four hundred five differentially expressed genes (DEGs) between the NFIL3-high and NFIL3-low groups were screened out using the "limma" R package. Enrichment analysis showed that most of the enriched genes were primarily involved in the TNF signaling pathway via NFκB, IL-17 signaling pathway, and rheumatoid arthritis pathways. Then, 10 genes (IL6, IL1β, CXCL8, CCL2, PTGS2, MMP3, MMP1, FOS, SPP1, and ADIPOQ) were identified as hub genes, and most of them play a key role in RA. Positive correlations between the hub genes and NFIL3 were revealed by qRT-PCR in RA PBMCs. An NFIL3-related protein-protein interaction (PPI) network was constructed using the STRING database, and four clusters (mainly participating in the inflammatory response, lipid metabolism process, extracellular matrix organization, and circadian rhythm) were constructed with MCODE in Cytoscape. Furthermore, 29 DEGs overlapped with RA-related genes from the RADB database and were mainly enriched in IL-17 signaling pathways. Thus, our study revealed the elevated expression of NFIL3 in both RA peripheral blood and synovial tissues, and the high expression of NFIL3 correlated with the abnormal inflammatory cytokines and inflammatory responses, which potentially contributed to RA progression.

Keywords: GEO; NFIL3; bioinformatics analysis; immune regulation; rheumatoid arthritis (RA).

Figure 1

Overall workflow for analyzing NFIL3 expression in rheumatoid arthritis.

Figure 2

NFIL3 expression in PBMCs from newly diagnosed RA patients and healthy controls. (A) Comparison of NFIL3 expression between RA and HC groups using qRT-PCR. (B) Differences in NFIL3 expression among RA patients with low, moderate, and high disease activity. (C) Alterations of NFIL3 expression before and after treatment. RA patients were divided into three groups according to DAS28-ESR score. Low disease activity, DAS28 ≤ 3.2; moderate disease activity, 3.2 < disease activity ≤ 5.1; high disease activity, DAS28 > 5.1. GAPDH was used as a reference gene for data normalization. PBMCs, peripheral blood mononuclear cells; RA, rheumatoid arthritis; HC, healthy control.

Figure 3

Correlations between NFIL3 expression and clinical indexes. (A) ESR, (B) RF, (C) anti-CCP, and (D) DAS28-ESR. ESR, erythrocyte sedimentation rate; RF, rheumatoid factor; anti-CCP, anti-cyclic citrullinated peptide; DAS28-ESR, disease activity score with 28 joints using erythrocyte sedimentation rate.

Figure 4

Identification of DEGs between the NFIL3-high and NFIL3-low groups and functional enrichment analysis. (A) Volcano plot shows the DEGs by comparing NFIL3-high and NFIL3-low synovial tissues from the GSE89408 dataset; red dot represents upregulated DEGs; blue dot represents downregulated DEGs; gray dot represents no significantly DEGs. (B) A heatmap of the 50 most significant DEGs according to the adjusted pvalue; blue indicates downregulated genes; red indicates upregulated genes. (C) GO enrichment and KEGG pathway analyses of the DEGs from DAVID database; the long bar represents the gene counts enriched in each pathway. (D) GSEA enrichment analysis shows that there are 15 upregulated and 4 downregulated gene sets with statistical significance between the high- and low-NFIL3 expression groups. (E) GSVA enrichment analysis of the 50 hallmark gene sets between the NFIL3-high and NFIL3-low groups. DEGs, differentially expressed genes; GSEA, gene set enrichment analysis; GSVA, gene set variation analysis. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 5

Construction of a PPI network of genes that coexpressed with NFIL3. (A) The PPI network with 112 nodes and 719 edges was constructed based on the STRING dataset and visualized by Cytoscape. (B–E) Four significant clusters were identified from the network by MCODE. The nodes with orange color represent the upregulated genes, while those with blue color represent the downregulated genes. The shape size of the nodes is arranged by the degree in the network. PPI, protein–protein interaction.

Figure 6

Association between NFIL3 and its coexpressed hub genes in RA. The eight hub genes and NFIL3 expression levels were analyzed in RA PBMCs by qRT-PCR and normalized to GAPDH. Spearman’s correlation analysis of NFIL3 and IL-6, IL1B, CXCL8, CCL2, PTGS2, MMP3, MMP1, and FOS in RA patients. RA, rheumatoid arthritis; PBMCs, peripheral blood mononuclear cells.