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Differentially Expressed Genes of Natural Killer Cells Can Distinguish Rheumatoid Arthritis Patients from Healthy Controls.

ABSTRACT: Rheumatoid arthritis (RA) is one of the most prevalent autoimmune diseases, while its molecular triggers are not fully understood. A few studies have shown that natural killer (NK) cells may play either a pathogenic or a protective role in RA. In this study, we sought to explore NK cell markers that could be plausibly used in evaluating the differences among healthy controls and RA patients. Publicly available transcriptome datasets from RA patients and healthy volunteers were analyzed, in order to identify differentially expressed genes (DEGs) between 1. different immune cells as compared to NK cells, and 2. NK cells of RA patients and healthy controls. The identified DEGs were validated using 16 healthy controls and 17 RA patients. Peripheral blood mononuclear cells (PBMCs) were separated by Ficoll density gradient method, while NK cells were isolated using RosetteSep technique. RNA was extracted and gene expression was assessed using RT-qPCR. All selected genes were differentially expressed in NK cells compared to PBMCs. CD56, CXCL16, PECAM-1, ITGB7, BTK, TLR10, and IL-1β were significantly upregulated, while CCL2, CCR4, RELA and IBTK were downregulated in the NK cells of RA patients when compared to healthy controls. Therefore, these NK specific genes might be used as promising biomarkers for RA diagnosis.

SUBMITTER: Elemam NM

PROVIDER: S-EPMC7290970 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundBatch effects due to sample preparation or array variation (type, charge, and/or platform) may influence the results of microarray experiments and thus mask and/or confound true biological differences. Of the published approaches for batch correction, the algorithm "Combating Batch Effects When Combining Batches of Gene Expression Microarray Data" (ComBat) appears to be most suitable for small sample sizes and multiple batches.MethodsSynovial fibroblasts (SFB; purity > 98%) were obtained from rheumatoid arthritis (RA) and osteoarthritis (OA) patients (n = 6 each) and stimulated with TNF-α or TGF-β1 for 0, 1, 2, 4, or 12 hours. Gene expression was analyzed using Affymetrix Human Genome U133 Plus 2.0 chips, an alternative chip definition file, and normalization by Robust Multi-Array Analysis (RMA). Data were batch-corrected for different acquiry dates using ComBat and the efficacy of the correction was validated using hierarchical clustering.ResultsIn contrast to the hierarchical clustering dendrogram before batch correction, in which RA and OA patients clustered randomly, batch correction led to a clear separation of RA and OA. Strikingly, this applied not only to the 0 hour time point (i.e., before stimulation with TNF-α/TGF-β1), but also to all time points following stimulation except for the late 12 hour time point. Batch-corrected data then allowed the identification of differentially expressed genes discriminating between RA and OA. Batch correction only marginally modified the original data, as demonstrated by preservation of the main Gene Ontology (GO) categories of interest, and by minimally changed mean expression levels (maximal change 4.087%) or variances for all genes of interest. Eight genes from the GO category "extracellular matrix structural constituent" (5 different collagens, biglycan, and tubulointerstitial nephritis antigen-like 1) were differentially expressed between RA and OA (RA > OA), both constitutively at time point 0, and at all time points following stimulation with either TNF-α or TGF-β1.ConclusionBatch correction appears to be an extremely valuable tool to eliminate non-biological batch effects, and allows the identification of genes discriminating between different joint diseases. RA-SFB show an upregulated expression of extracellular matrix components, both constitutively following isolation from the synovial membrane and upon stimulation with disease-relevant cytokines or growth factors, suggesting an "imprinted" alteration of their phenotype.

Project description:Objective: Early treatment-naïve rheumatoid arthritis (RA) has defective regulatory T (Treg) cells and increased inflammation response. In this study, we aim to illustrate the regulation of Treg cells in pathogenesis of early rheumatoid arthritis by arsenic trioxide (As2O3). Methods: We studied the effects of As2O3 on gene expression in early treatment-naïve RA Treg cells with single cell RNA-seq (scRNA-seq). Treg cells were sorted from peripheral blood mononuclear cells (PBMCs) and purified by fluorescence-activated cell sorting (FACS) and cultured with or without As2O3 (at 0.1 µM) for 24 h. Total RNA was isolated and sequenced, and functional analysis was performed against the Gene Ontology (GO) database. Results for selected genes were confirmed with RT-qPCR. Results: As2O3 exerts no significant effect on CD4+ T-cell apoptosis under physical condition, and selectively modulate CD4+ T cells toward Treg cells not Th17 cells under special polarizing stimulators. As2O3 increased the expression of 200 and reduced that of 272 genes with fold change (FC) 2.0 or greater. Several genes associated with inflammation, Treg-cell activation and differentiation as well as glucose and amino acids metabolism were among the most strongly affected genes. GO function analysis identified top ten ranked significant biological process (BPs), molecular functions (MFs), and cell components (CCs) in treatment and nontreatment Treg cells. In GO analysis, genes involved in the immunoregulation, cell apoptosis and cycle, inflammation, and cellular metabolism were enriched among the significantly affected genes. The KEGG pathway enrichment analysis identified the forkhead box O (FoxO) signal pathway, apoptosis, cytokine-cytokine receptor interaction, cell cycle, nuclear factor-kappa B (NF-κB) signaling pathway, tumor necrosis factor α (TNF-α), p53 signaling pathway, and phosphatidylinositol 3'-kinase (PI3K)-Akt signaling pathway were involved in the pathogenesis of early treatment-naïve RA. Conclusion: This is the first study investigating the genome-wide effects of As2O3 on the gene expression of treatment-naïve Treg cells. In addition to clear anti-inflammatory and immunoregulation effects, As2O3 affect amino acids and glucose metabolism in Treg cells, an observation that might be particularly important in the metabolic phenotype of treatment-naïve RA.

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Differentially Expressed Genes of Natural Killer Cells Can Distinguish Rheumatoid Arthritis Patients from Healthy Controls.

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