Analysis of Novel Immunological Biomarkers Related to Rheumatoid Arthritis Disease Severity

Abstract

Rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPAs) are the most frequently used rheumatoid arthritis (RA) diagnostic markers, but they are unable to anticipate the patient's evolution or response to treatment. The aim of this study was to identify possible severity biomarkers to predict an upcoming flare-up or remission period. To address this objective, sera and anticoagulated blood samples were collected from healthy controls (HCs; n = 39) and from early RA (n = 10), flare-up (n = 5), and remission (n = 16) patients. We analyzed leukocyte phenotype markers, regulatory T cells, cell proliferation, and cytokine profiles. Flare-up patients showed increased percentages of cluster of differentiation (CD)3⁺CD4^- lymphocytes (p < 0.01) and granulocytes (p < 0.05) but a decreased natural killer (NK)/T lymphocyte ratio (p < 0.05). Analysis of leukocyte markers by principal component analysis (PCA) and receiver operating characteristic (ROC) curves showed that CD45RO⁺ (p < 0.0001) and CD45RA⁺ (p < 0.0001) B lymphocyte expression can discriminate between HCs and early RA patients, while CD3⁺CD4^- lymphocyte percentage (p < 0.0424) and CD45RA⁺ (p < 0.0424), CD62L⁺ (p < 0.0284), and CD11a⁺ (p < 0.0185) B lymphocyte expression can differentiate between flare-up and RA remission subjects. Thus, the combined study of these leukocyte surface markers could have potential as disease severity biomarkers for RA, whose fluctuations could be related to the development of the characteristic pro-inflammatory environment.

Keywords: B lymphocytes; NK/T lymphocyte ratio; biomarkers; central memory cells; cytotoxic T lymphocytes; disease severity; effector memory cells; leukocyte phenotype; rheumatoid arthritis.

Figure 1

Leukocyte population gating strategy. Lymphocytes, monocytes, and granulocytes were gated based on size and granularity. Different lymphocyte subpopulations were identified according to cluster of differentiation (CD)3, CD4, CD16, CD56, and CD19 expression. Pseudo-colors in plots represent cell density.

Figure 2

Analysis of leukocyte populations. Total lymphocytes (A), T lymphocytes (B), CD3⁺CD4⁻ lymphocytes (C), T helper (Th) lymphocytes (D), natural killer (NK) cells (E), NK cell/T lymphocyte ratio (F), B lymphocytes (G), monocytes (H), and granulocytes (I) were measured in HCs, ERA, flare-up, and remission patients. Bars show mean ± SEM. p-values were calculated using ANOVA (Fisher’s LSD test) and the Kruskal–Wallis test (uncorrected Dunn’s test). * = p < 0.05; ** = p < 0.01.

Figure 3

Analysis of leukocyte marker expression. Expression level of epidermal growth factor (EGFR), endothelial protein C receptor (EPCR), CD16, CD32, CD64, CD45RA, CD45RO, CD62L, and CD11a in total (A), T (B), and Th (C) lymphocytes, CD3⁺CD4⁻ lymphocytes (D), NK cells (E), B lymphocytes (F), monocytes (G), and granulocytes (H) in study subjects. XY scatter plot showing the principal component analysis (PCA), performed using 95% confidence ellipses for HC (in red) and RA patients (in blue). Matrix plot represents the correlation of the expression of the different leukocyte markers in HCs, ERA, flare-up (FU), and remission (REM) patients. Higher correlations in the matrix plot are shown in blue.

Figure 4

Receiver operating characteristic (ROC) curves of leukocyte antigens. Analysis of discrimination between HCs vs. ERA patients (A) and flare-up vs. remission patients (B) using CD3⁺CD4⁻ lymphocytes, NK cells, NK/T lymphocyte ratio, granulocytes, CD45RO⁺, CD45RA⁺, CD62L⁺, and CD11a⁺ B lymphocytes, ACPA, RF, CRP, and DAS28. p-values and the area under the curve (AUC) for each of the ROC curves were calculated using the Wilson/Brown method.

Figure 5

Analysis of exosomal miRNA (miR) levels. Serum exosomal miR-21, miR-146a, and miR-155 expression was measured (in ΔΔCt) in ERA, flare-up, and remission patients. Bars show mean. p-values were calculated using two-way ANOVA (Tukey’s test).