Aberrant Subsets of Regulatory T Cells and their Correlations with Serum IL-2 in Patients with Rheumatoid Arthritis

Abstract

Aberrant number and/or dysfunction of regulatory T cells (Tregs) is associated with the development of rheumatoid arthritis (RA). This study aimed to assess the frequencies of naive Tregs (nTregs) and memory Tregs (mTregs) in the peripheral blood of RA patients and to explore their relationships with cytokine levels. This study involved 97 RA patients categorized into three groups based on Disease Activity Score 28 (DAS28) and 50 healthy controls (HCs). Flow cytometry was employed to quantify Treg subsets in peripheral blood, while serum cytokine concentrations were measured using a flow cytometry bead array. The findings revealed that three RA groups, stratified by disease activity, all exhibited a significant decrease in both the count and percentage of nTregs and an increase in the percentage of mTregs compared to HCs. Notably, the group with high RA disease activity displayed a higher percentage of mTregs than the remission group. Additionally, correlation analysis indicated that IL-2 concentrations were negatively correlated with total T, CD4 + T and Th17 cell counts, and positively correlated with the absolute count of nTregs. This study demonstrated that the count of mTregs in RA patients increased with escalating disease activity, while the count of nTregs remained unchanged. Moreover, IL-2 concentrations were positively correlated with the numbers of Tregs and nTregs, suggesting that IL-2 plays a significant role in modulating Treg subsets. Further studies on targeted therapies aligned with the distribution of mTregs and nTregs in RA patients with varying disease activity could potentially achieve effective remission.

Introduction

Rheumatoid arthritis (RA) is a chronic autoimmune disorder marked by inflammatory synovitis, leading to articular cartilage degradation and subsequent bone erosion [1]. Timely and effective intervention is crucial, as untreated RA can lead to progressive joint damage, compromised joint function, and a significant reduction in quality of life [2]. The pathogenesis of RA is increasingly recognized to involve a dysregulation of CD4⁺T cell subsets, with accumulating evidence underscoring the pivotal imbalance between regulatory T cells (Tregs) and T helper 17 (Th17) cells [3, 4]. Th17 cells are known to drive synovial inflammation through the production of a range of pro-inflammatory cytokines, whereas Tregs play a crucial role in suppressing inflammation and maintaining immune homeostasis [5]. The breakdown of immune tolerance, leading to the abnormal activation of various immune and non-immune cell types, is a key characteristic in the initiation and progression of RA [6].

Tregs, a specialized subset of CD4⁺T cells, play a pivotal role in preserving immune tolerance, modulating immune responses, facilitating tissue repair, and curbing pathogenic inflammation [7]. Tregs secrete anti-inflammatory cytokines such as interleukin-10 (IL-10) and TGF-β to inhibit the proliferation of T effector cells (Teffs) [8, 9]. Aberrant number and/or dysfunction of Tregs, alongside a surge in cytokine levels, can trigger various autoimmune diseases, including RA [5, 10]. Generally, Tregs are classified into two main categories based on their developmental origin: thymically derived Tregs and peripherally induced Tregs [9, 10]. Thymically-derived Tregs and peripherally-induced Tregs both possess the functions of immune suppression and the maintenance of immune tolerance [11]. Nonetheless, the classification of Treg subsets extends beyond their origins to encompass their surface markers and functional characteristics [12]. In humans, Tregs are categorized into CD45RA + naive Tregs (nTregs) and CD45RO + memory Tregs (mTregs) based on the expression of the cellular memory surface marker CD45RO [13]. nTregs, predominantly thymically derived, are characterized as cells that have not encountered their cognate antigen in the periphery or despite continuous exposure to antigens, interact at a level that is insufficient for full activation [13, 14]. mTregs are uniquely induced in response to antigenic stimulation, constituting an activated subset of Tregs with enhanced local tropism and migratory capabilities compared to nTregs, and mTregs exhibit the ability to persist for substantial periods even in the absence of antigens [13, 15–17]. Peripherally induced Treg cell populations are also capable of generating mTregs in vivo [13]. Approximately 80% of Tregs in neonatal umbilical cord blood are nTregs, likely attributable to the restricted prenatal antigen exposure, which is primarily mediated through maternal transmission via the placenta. With advancing age, the proportion of mTregs increases within the Treg repertoire as a result of exposure to a variety of environmental antigens, ultimately predominating over nTregs in the elderly [12]. The role of mTregs during pregnancy has garnered significant interest [12, 18], but their relevance in RA remains elusive.

Numerous studies have indicated that there is a decrease in the frequency of circulating Tregs in RA patients compared to healthy controls [19, 20]. Recent research has begun to elucidate the abnormalities within Treg cell subsets in RA, predominantly utilizing animal models [10]. However, the distribution and specific roles of Treg subsets in RA patients remain to be fully clarified. In this retrospective analysis, we examined the lymphocyte subsets, CD4⁺T and Treg cell populations in peripheral blood, as well as the concentrations of various serum cytokines, among RA patients with varying degrees of disease activity. Consequently, this study aimed to determine the counts of nTregs and mTregs in peripheral blood of RA patients, and to explore their associations with serum cytokine levels. Through this investigation, we sought to delineate the role of nTregs and mTregs in RA pathogenesis and establish a foundation for the development of targeted therapies.

Materials and Methods

Patients

In the retrospective study, 97 patients with RA were enrolled from October 2022 to May 2023 at the Rheumatology Department of the Second Hospital of Shanxi Medical University. The inclusion criteria for all patients were based on adherence to the revised 2010 American College of Rheumatology/European League against Rheumatism classification criteria for RA [21]. Patients with other autoimmune diseases, malignancies, severe infections, or those who were pregnant or receiving low-dose human recombinant IL-2 therapy were excluded from the study. Additionally, 50 healthy controls (HCs), matched for age and sex to the RA patients, were recruited from the Physical Examination Center of our hospital. On the morning of patient’s consultation, all blood samples for clinical indicators were collected following an overnight fast. The present study was approved by the Ethics Committee of the Second Hospital of Shanxi Medical University [Approval (2019) YX No. (105)].

Clinical and Laboratory Data Collection

We retrospectively collected clinical and laboratory parameters of all patients with RA. The features on gender, age, BMI, RA duration, family history and current treatment were collected. Serological indicators, including erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), routine blood counts, coagulation function, liver function, renal function, antiperinuclear factor, antikeratin antibody, antinuclear antibodies, rheumatoid factor (RF), anti-mutated citrullinated vimentin (anti-MCV), anti-cyclic citrullinated peptide (anti-CCP) antibody, were all detected. Disease activity in patients with RA was assessed using the Disease Activity Score 28 joint count (DAS28)-ESR. RA patients were categorized into three groups based on their DAS28-ESR scores: the disease remission group (Re-RA) with scores ≤ 2.6, the low-moderate disease activity group (L-MA-RA) with scores between 2.6 and 5.1, and the high disease activity group (HA-RA) with scores > 5.1.

Flow Cytometry Measurement of Absolute Number of Peripheral Lymphocytes, CD4⁺T and Treg Cell Subsets

Flow cytometry was employed to characterize the immunophenotypes of lymphocytes, CD4⁺T subsets, nTregs and mTregs in peripheral blood. Initially, peripheral blood samples (2 ml) were collected in heparin anticoagulation tubes. All the monoclonal antibodies conjugated with fluorescein isothiocyanate (FITC), phycoerythrin (PE), peridinin chlorophyll protein (PerCP), Brilliant Violet-421 (BV421), and allophycocyanin (APC) were purchased from BD Biosciences.

1. Detection of T, B, natural killer (NK), CD4⁺T and CD8⁺T cells

Two BD Trucount tubes (A and B) containing a known number of fluorescent beads were consecutively numbered, and 50 μL of peripheral blood sample were added to each one by reverse loading. 20 μL anti-human CD3-FITC/CD8-PE/CD45-PerCP/CD4-APC antibodies (cat number: 662965) were added into tube A, while 20 μL anti-human CD3-FITC/CD16+CD56-PE/CD45-PerCP/CD19-APC antibodies (cat number: 662965) were added to tube B. The mixture was shake-incubated at 25 °C for 20 min in the dark. Subsequently, 450 μL 1X FACS hemolysin was added into the mixture, and incubated at 25 °C in the dark for 15 min. Then, samples were washed with phosphate buffered saline (PBS) and tested on the machine within 24 h. For each sample, a total of 15,000 cells were obtained for analysis.

• (2).Detection of CD4⁺T cell subsets

Firstly, 80 μL of peripheral blood was collected and fully mixed with 10 μL of phorbol myristate acetate (PMA) working solution, 10 μL of ionomycin working solution and 1 μL of GolgiStop, and the mixture was incubated for 5 h at 37 °C and 5% CO2 atmosphere. After staining the cells with anti-CD4-FITC (cat number: 340133) antibody at 25 °C for 30 min in the dark, 1 mL of freshly prepared fixation/permeabilization solution was added into the mixture and incubated for 30 min at 4 °C in the dark. Th1 cells were identified using anti-CD4-FITC antibody and anti-IFN-γ-APC antibody (cat number: 341117). Th2 cells were detected with anti-CD4-FITC antibody and anti-IL-4-PE antibody (cat number: 340451), and Th17 cells were characterized by anti-CD4-FITC antibody and anti-human IL-17A-PE antibody (cat number: 560436).

• (3).Detection of Treg cell subsets

The identification of Treg cell subsets was conducted using a methodological approach similar to that utilized for the assessment of lymphocyte subpopulations. Following treatment with red blood cell lysis buffer, the cells were stained with anti-CD4-PerCP antibody (cat number: 340671), anti-CD25-APC antibody (cat number: 340939) and anti-human CD127-BV421 antibody (cat number: 562436) for Tregs analysis; anti-CD4-PerCP antibody, anti-CD25-APC antibody, anti-human CD127-BV421 antibody and anti-CD45RA-FITC antibody (cat number: 347723) for nTregs; and anti-CD4-PerCP antibody, anti-CD25-APC antibody, anti-human CD127-BV421 antibody and anti-CD45RO-PE antibody (cat number: 347967) for mTregs.

All of the samples were mixed, incubated, and washed in accordance with the manufacturer's instructions (BD Biosciences). Finally, automatic detection of the samples was performed using BD Multitest software (BD Biosciences). The absolute number of CD4⁺T subsets = the percentage of each CD4⁺T subset * the absolute number of total CD4⁺T cells (cells/μL). The absolute number of Tregs was the sum of the absolute number of nTregs and mTregs (Fig. 1).

Fig. 1.

A schematic representation of the gating strategy for flow cytometric analysis. (A) Representative flow cytometry analysis of peripheral lymphocytes. T cell: CD45⁺CD3⁺; B cell: CD45⁺CD3⁻CD19⁺; NK cell: CD45⁺CD3⁻CD16⁺CD56⁺; CD4⁺T cell: CD45⁺CD3⁺CD4⁺; CD8⁺T cell: CD45⁺CD3⁺CD8⁺. (B) Representative flow cytometry analysis of CD4⁺T cell subsets. Th1 cell: CD4⁺INF-γ⁺; Th2 cell: CD4⁺IL-4⁺; Th17 cell: CD4⁺IL-17⁺. (C) Representative flow cytometry analysis of Treg cell subsets. Treg: CD4⁺CD25⁺CD127^low; nTreg: CD4⁺CD25⁺CD127^lowCD45RA⁺; mTreg: CD4⁺CD25⁺CD127^lowCD45RO⁺

Detection of Cytokine Concentrations by Cytometric Bead Array

Serum was separated from 4 ml venous blood and stored at −20 °C. Concentrations of the serum IL-2, soluble interleukin-2 receptor (sIL-2R), IL-4, IL-6, IL-10, IL-17, interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α) were also detected by flow cytometry. A cytometric bead array (CBA) kit was purchased from Jiangsu Saiqi Biotechnology Co., Ltd. (Jiangsu, China), and utilized according to the manufacturer's instructions. The results were presented in pg/mL.

Statistical Analysis

SPSS ver. 23.0 (SPSS Inc., Chicago, IL, USA) and GraphPad Prism ver. 9.0 (GraphPad Software Inc., San Diego, CA, USA) were utilized for descriptive and inferential statistics. For continuous variables, data were expressed as mean ± standard deviation for normally distributed data and as median (interquartile range) for non-normally distributed data. Non-normally distributed data comparisons were performed using the Mann–Whitney U test or the Kruskal–Wallis H test, while normally distributed data were evaluated using the independent samples t-test or analysis of variance (ANOVA). Categorical variables were expressed as frequencies, and statistical comparisons were performed using chi-square test or Fisher’s exact test. The Spearman's correlation analysis was employed to elucidate the correlation between two research indicators. A p-value < 0.05 (two-sided) was considered as statistically significant.

Results

Clinical and Demographic Features

The primary demographic features, disease characteristics, laboratory statistics and current treatment of the 97 RA patients (25 Re-RA, 56 L-MA-RA and 16 HA-RA) are presented in Table 1. Comparisons between the three RA groups revealed that HA-RA patients exhibited lower concentrations of hemoglobin and higher concentrations of CRP compared to Re-RA patients, while Re-RA patients had higher haemoglobin concentrations than L-MA-RA patients. There was no significant difference in demographic features, percentage of current medication, or serologic autoantibody indicators (RF, anti-MCV, and anti-CCP antibody), or liver and renal function among the RA groups.

Table 1.

Characteristics of RA patients in each disease activity group

	Remission group (A) (n = 25)	Low-Moderate activity group (B) (n = 56)	High activity group (C) (n = 16)	P-value	P-value, A vs. B	P-value, B vs. C	P-value, A vs. C
Demographic
Male, n (%)	7.00(28.00%)	7.00(12.50%)	4.00(25.00%)	0.195	-	-	-
Female, n (%)	18.00(72.00%)	49.00(87.50%)	12.00(75.00%)
Age (years)a	51.44 ± 14.25	51.66 ± 12.29	58.50 ± 8.59	0.128	-	-	-
BMI (kg/m2)b	21.64(20.06–25.46)	21.81(20.30–23.80)	22.68(21.03–24.25)	0.876	-	-	-
Disease duration (months)b	52.00(36.00–98.00)	60.00(45.50–120.00)	88.50(43.00–138.50)	0.713	-	-	-
Family history, n (%)	0.00(0.00%)	0.00(0.00%)	0.00(0.00%)	1.000	-	-	-
Lifestyle
Smoking, n (%)	4.00(16.00%)	4.00(7.10%)	4.00(25.00%)	0.131	-	-	-
Drinking, n (%)	3.00(12.00%)	1.00(1.80%)	0.00(0.00%)	0.068	-	-	-
Laboratory characteristics
ESR (mm/h)b	7.00(5.00–13.00)	18.50(14.00–29.00)	41.50(38.50–57.00)	< 0.001***	< 0.001***	< 0.001***	< 0.001***
CRP (mg/ml)b	3.14(1.91–3.18)	3.72(3.12–6.03)	6.23(3.13–22.23)	0.010*	0.115	0.341	0.009 **
WBC (*10^9/L)b	5.72(4.63–6.79)	6.13(5.15–7.68)	6.54(5.74–7.74)	0.269	-	-	-
LY (*10^9/L)b	1.82(1.44–1.98)	1.63(1.25–1.95)	1.60(1.19–2.67)	0.456	-	-	-
Hb (g/L)b	143.00(137.00–150.00)	132.00(123.50–140.50)	134.00(122.00–138.50)	0.004**	0.005**	1.000	0.037*
PLT (*10^9/L)a	226.36 ± 58.41	249.23 ± 67.18	247.06 ± 64.16	0.330	-	-	-
Concentrations of blood coagulation
PT (s)b	14.00(13.70–14.70)	13.75(12.80–14.50)	13.85(12.05–14.50)	0.310	-	-	-
APTT (s)a	32.24 ± 4.00	30.40 ± 3.50	30.93 ± 2.41	0.097	-	-	-
D-dimer (ug/L)b	138.00(89.00–277.00)	171.50(81.50–328.00)	340.00(109.50–708.50)	0.232	-	-	-
Fibrinogen (mmol/L)b	3.18(2.78–4.07)	3.28(2.50–3.75)	3.47(2.73–5.06)	0.418	-	-	-
Liver function
ALT (U/L)b	16.70(12.30–25.30)	19.25(13.20–25.40)	17.95(14.40–24.95)	0.941	-	-	-
AST (U/L)a	22.99 ± 6.73	22.89 ± 6.48	22.25 ± 6.17	0.930	-	-	-
Renal function
BUN (mmol/L)b	4.70(4.10–5.10)	4.95(3.95–5.75)	5.10(4.20–6.50)	0.443	-	-	-
Cr (μmol/L)b	59.00(53.00–71.00)	59.00(51.00–64.50)	60.50(52.00–67.50)	0.900	-	-	-
DAS-28- ESRb	1.92(1.50–2.32)	3.70(3.22–4.12)	5.23(5.16–5.83)	< 0.001***	< 0.001***	< 0.001***	< 0.001***
Autoantibody
RF (U/m L)b	110.90(11.40–253.90)	58.15(15.70–300.00)	189.20(40.80–300.00)	0.466	-	-	-
Anti-CCP (U/mL)b	207.25(7.00–500.00)	391.05(44.63–500.00)	344.49(105.45–500.00)	0.421	-	-	-
Anti- MCV (U/mL)b	73.10(0.00–264.90)	113.80(0.00–459.95)	278.85(139.15–770.35)	0.188	-	-	-
APF, n (%)	9.00(36.00%)	23.00(41.10%)	8.00(50.00%)	0.673	-	-	-
AKA, n (%)	8.00(32.00%)	25.00(44.60%)	8.00(50.00%)	0.449	-	-	-
ANA, n (%)	7.00(28.00%)	30.00(53.60%)	8.00(50.00%)	0.098	-	-	-
Current medications
NSAIDs, n (%)	9.00(36.00%)	27.00(48.20%)	9.00(56.30%)	0.410	-	-	-
Oral prednisone (≤ 10 mg/day), n (%)	4.00(16.00%)	15.00(26.80%)	8.00(50.00%)	0.058	-	-	-
DMARDs, n (%)	16.00(64.00%)	46.00(82.10%)	14.00(87.50%)	0.117	-	-	-

Abbreviations: BMI: body mass index; ESR: erythrocyte sedimentation rate; CRP: C-reactive protein; WBC: white blood cell; LY: lymphocyte; Hb: hemoglobin; PLT: platelet; PT: prothrombin time; APTT: activated partial thromboplastin time; ALT: alanine transaminase; AST: aspartic transaminase; BUN: blood urea nitrogen; Cr: creatinine; DAS-28: Disease Activity Score 28; RF: rheumatoid factor; Anti-CCP: anti-cyclic citrullinated peptide antibody; Anti-MCV: anti-mutated citrullinated vimentin; APF: antiperinuclear factor; AKA: antikeratin antibody; ANA: antinuclear antibodies; NSAIDs: nonsteroidal antiinflammatory drugs; DMARDs: disease-modifying antirheumatic drugs

^aResults are expressed as the mean ± standard deviation

^bResults are expressed as the median and 25th and 75th percentiles

^* P < 0.05, ** P < 0.01, *** P < 0.001

Increased Th17/Treg Ratio in RA Patients Due to Decreased Tregs and Increased Th17 Cell Counts

We determined the absolute numbers and percentages of T, B, NK cells and CD4⁺T subsets in the peripheral blood of each group. No significant differences were observed in the absolute counts or percentages of T, B, NK cells, and CD8⁺T cells among the Re-RA, L-MA-RA, HA-RA patient groups and HCs (Fig. 2A and C). However, patients with RA exhibited a significantly higher proportion of CD4⁺T cells compared to HCs (Table 2). Initially, we assessed CD4⁺T cell subsets, including Th1, Th2, Th17 cells, and Tregs, between RA patients and HCs. Compared to HCs, RA patients demonstrated a significantly increased absolute number and percentage of Th17 cells, while the absolute count of Tregs was significantly reduced in RA patients. Additionally, the ratio of Th17/Treg was significantly higher in RA patients compared to HCs (0.33 vs. 0.17, P < 0.001). There was no significant difference in the counts and percentages of Th1 and Th2 cells between RA patients and HCs (Table 3). These findings suggest that the Th17/Treg imbalance may play a pivotal role in the pathogenesis of RA.

Fig. 2.

The absolute count and percentage of peripheral lymphocytes, and CD4⁺T cell subsets in three RA groups and HCs. HCs: healthy controls. T: T lymphocyte; B: B lymphocyte; NK: natural killer cell; Th1: T-helper 1 cells; Th2: T-helper 2 cells; Th17: T-helper 17 cells; Treg: regulatory T cells. (Kruskal–Wallis H test was utilized for comparative analysis. * P < 0.05, ** P < 0.01, *** P < 0.001)

Table 2.

Absolute counts and proportions of lymphocyte in the peripheral blood in RA and HCs

	RA (n = 97)	HCs (n = 50)	P-value
Total T (cells/μL)b	1184.48(861.78–1449.19)	1173.98(1054.13–1417.00)	0.322
T%a	70.66 ± 9.97	71.98 ± 6.72	0.346
Total B (cells/μL)b	165.44(98.19–260.52)	174.94(132.00–229.72)	0.683
B%b	10.38(7.04–14.66)	10.77(8.01–13.00)	0.886
NK (cells/μL)b	199.98(126.30–366.15)	239.45(179.00–359.01)	0.202
NK%b	14.27(8.84–23.59)	14.82(11.10–18.28)	0.553
CD4+T (cells/μL)b	698.04(507.17–867.07)	609.00(534.13–731.00)	0.133
CD4+T%a	42.35 ± 8.92	37.50 ± 7.51	0.001**
CD8+T (cells/μL)b	392.35(272.77–543.46)	448.13(356.03–556.00)	0.095
CD8+T%a	26.03 ± 9.05	27.64 ± 8.11	0.293
CD4+T/CD8+Tb	1.67(1.24–2.4)	1.38(1.03–1.96)	0.028*

Abbreviations: HC: Healthy control; T: T lymphocyte; B: B lymphocyte; NK: natural killer cell

^aResults are expressed as the mean ± standard deviation

^bResults are expressed as the median and 25th and 75th percentiles

^* P < 0.05, ** P < 0.01, *** P < 0.001

Table 3.

Absolute counts and proportions of CD4⁺T and Treg subsets in the peripheral blood in RA and HCs

	RA (n = 97)	HCs (n = 50)	P-value
Th1 (cells/μL)b	101.86(56.00–151.77)	91.96(31.71–140.58)	0.084
Th1%b	14.84(9.14–22.04)	15.34(6.34–22.36)	0.335
Th2 (cells/μL)b	6.85(4.96–10.57)	7.09(4.72–9.67)	0.582
Th2%b	1.11(0.84–1.57)	1.18(0.81–1.55)	0.886
Th1/Th2b	13.28(8.05–19.63)	11.84(4.70–19.03)	0.169
Th17 (cells/μL)b	7.93(4.99–12.94)	5.24(3.48–6.40)	< 0.001***
Th17%b	1.27(0.84–1.83)	0.85(0.57–1.01)	< 0.001***
Treg (cells/μL)b	25.99(19.95–36.49)	31.55(25.91–43.9)	< 0.006**
Treg %b	4.88(4.01–5.84)	4.60(3.90–5.10)	0.188
Th17/Tregb	0.33(0.21–0.57)	0.17(0.11–0.21)	< 0.001***
nTreg (cells/μL)b	6.78(4.33–11.27)	13.89(10.33–20.13)	< 0.001***
nTreg %b	1.26(0.75–1.87)	1.90(1.50–2.60)	< 0.001***
mTreg (cells/μL)b	18.45(13.04–24.87)	17.33(14.64–23.78)	0.622
mTreg %b	3.35(2.71–4.11)	2.50(2.10–3.10)	< 0.001***

Abbreviations: HCs: healthy controls. Th1: T-helper 1 cells; Th2: T-helper 2 cells; Th17: T-helper 17 cells; Treg: regulatory T cells; nTreg: naive regulatory T cells; mTreg: memory regulatory T cells. ^bResults are expressed as the median and 25th and 75th percentiles

^* P < 0.05, ** P < 0.01, *** P < 0.001

Subsequently, we compared the absolute numbers and percentages of CD4⁺T cell subsets among Re-RA, L-MA-RA, HA-RA, and HCs. There were no significant differences in the numbers and percentages of Th1 and Th2 cells, the Th1/Th2 ratio, or the percentage of Tregs among the four groups. The absolute number and percentage of Th17 cells, and the Th17/Treg ratio were all significantly higher in three RA groups than in HCs. However, there were no significant differences in these parameters among the three RA subgroups. Notably, the absolute number of Tregs was significantly reduced in L-MA-RA patients compared to HCs, whereas no significant difference was observed between Re-RA, HA-RA patients, and HCs in terms of Treg counts (Fig. 2B and C). These results implied that the number of Tregs may dynamically alter in response to different disease activity in RA.

RA Patients Showed Decreased nTregs and Increased mTregs Proportions Relative to HCs

In comparing Treg subsets between the RA patients and HCs, RA patients demonstrated a significantly reduced absolute number and percentage of nTregs. Furthermore, the percentage of mTregs was significantly higher in RA patients, while the absolute count of mTregs was comparable to that observed in HCs (Table 3). We proceeded to investigate the distribution of Treg subsets in RA patients stratified by varying levels of disease activity. Compared to HCs, all three RA groups showed a significantly lower absolute count and percentage of nTregs. However, no significant differences were observed in these parameters among the three RA groups (Fig. 3A and B). Moreover, there was no significant difference in the absolute number of mTregs among the three RA groups and the HCs (Fig. 3C). Nevertheless, the percentage of mTregs was significantly elevated in all three RA groups compared to HCs, and there was a trend indicating that the percentage of mTregs increased with higher RA disease activity. Additionally, HA-RA patients had a significantly higher percentage of mTregs compared to those with Re-RA (Fig. 3D).

Fig. 3.

The number and percentage of Treg cell subsets in three RA groups and HCs. HCs: healthy controls. Treg: regulatory T cells; nTreg: naive regulatory T cells; mTreg: memory regulatory T cells. (Kruskal–Wallis H test was utilized for comparative analysis. * P < 0.05, ** P < 0.01, *** P < 0.001)

Various Cytokine Concentrations Were Increased in RA Patients

We compared the levels of various serum cytokines among the Re-RA, L-MA-RA, HA-RA patients and HCs. Compared to HCs, the concentrations of IL-2, sIL-2R, IL-4, IL-6, IL-10, IL-17, IFN-γ, and TNF-α were significantly elevated in all three RA groups. However, the concentrations of these cytokines were not statistically different among the Re-RA, L-MA-RA, HA-RA patients (Fig. 4).

Fig. 4.

The concentrations of various serum cytokines (pg/ml) in three RA groups and HCs. HCs: healthy controls. IL: interleukin; sIL-2R: soluble interleukin-2 receptor; IFN-γ: interferon-γ; TNF-α: tumor necrosis factor-α. (Kruskal–Wallis H test was utilized for comparative analysis. * P < 0.05, ** P < 0.01, *** P < 0.001)

Correlation Analysis of Serum IL-2 Concentrations with General Clinical Indicators, Peripheral Lymphocyte Counts, and Other Cytokine Levles

In the correlation analysis of serum cytokines with general clinical indicators in RA patients, serum IL-2 concentrations showed a negative correlation with white blood cell (WBC) counts and fibrinogen levels. Conversely, sIL-2R concentrations were positively correlated with DAS28, ESR, CRP, and fibrinogen levels (Fig. 5A). Upon analyzing the correlation between serum cytokine levels and peripheral lymphocyte counts, we observed that IL-2 concentrations negatively correlated with the counts of T, CD4⁺T, and Th17 cells, and positively correlated with the percentage of Tregs and the absolute number of nTregs. Additionally, sIL-2R concentrations were found to negatively correlate with the counts of T and CD4⁺T cells, and positively correlated with the percentage of Tregs and the counts of mTregs (Fig. 5B). Then, IL-4 concentrations showed a negative correlation with WBC counts, as well as the numbers of T, CD4⁺T cells and Th17 cells. IL-6 concentrations exhibited a positive correlation with CRP concentrations, WBC counts and D-dimer concentrations, while they were inversely related to with the numbers of B, Th17 cells and the Th17/Treg ratio. Furthermore, IL-10 concentrations exhibited a positive correlation with the percentages of Tregs and mTregs, whereas IL-17 concentrations showed a positive correlation with the concentrations of RF and anti-CCP, and the number of nTregs. The concentration of INF-γ was positively correlated with the concentrations of anti-CCP and anti-MCV, as well as the percentage of Tregs, and negatively correlated with the counts of CD4⁺T cells, Th2 cells, and Th17 cells. In addition, the concentration of TNF-α was negatively correlated with fibrinogen levels and the counts of CD4⁺T cells, Th2 cells, and Th17 cells (Fig. 5A and B).

Fig. 5.

Heat map of correlation of serum cytokine concentrations in RA patients. A: Heat map of correlation between cytokine concentrations and general clinical indicators. B: Heat map of correlation between cytokine concentrations and lymphocyte counts in peripheral blood. C: Heat map of Treg cell subsets and general clinical indicators. (The Spearman's correlation analysis was employed to elucidate the correlation between two research indicators. * P < 0.05, ** P < 0.01, *** P < 0.001)

Correlation analysis between Treg subsets and general clinical indicators demonstrated a positive association between the percentage of mTregs and disease activity. This correlation could potentially reflect the immune system's regulatory mechanisms or indicate a pathological alteration in Tregs (Fig. 5C). It underscores the importance of further research to elucidate the underlying mechanisms and to explore the therapeutic implications of this association.

Discussion

In this retrospective study, we examined the characteristics of Treg cell subsets and serum cytokine concentrations in patients with RA. RA patients exhibited a decrease in total Treg counts, a reduced absolute count and percentage of nTregs, and an increased percentage of mTregs. Furthermore, the count of mTregs in RA patients increased with escalating disease activity, while nTreg counts remained unchanged. Correlation analysis revealed that IL-2 concentrations positively correlated with the Treg percentage and the absolute count of nTregs. These results suggest that nTregs and mTregs contribute to RA pathogenesis.

Elevated Th17 cell counts and reduced Treg numbers contribute to the Th17/Treg imbalance in RA patients, indicating a role for this imbalance in RA progression [19]. Our study demonstrated a significant increase in Th17 cell counts and a decrease in Treg numbers in RA patients compared to HCs. Similar to our results, many studies observed that RA patients displayed lower the percentage of circulating Tregs than HCs [22–24]. Furthermore, it was reported that the expression of FoxP3 (a key marker for Tregs) was the lowest in active RA and the highest in inactive RA, whereas RORγt (the Th17 transcription factor) presented a reverse trend compared to HCs [25]. As their mediation of osteoclastogenesis, synovial neoangiogenesis and bone erosion, pathogenic Th17 cells play a significant role in the pathogenesis of RA [26]. Tregs are known to prevent aberrant Teff autoreactive reactions and sustain immune self-tolerance [27], whereas the pathogenesis and progression of RA have been associated with functional abnormalities and reductions in the proportion of Tregs [20, 28].

Tregs, identified by the surface markers CD3, CD4, CD25, and intracellular FoxP3, along with the absence or low expression of CD127, play a significant role in immune homeostasis maintenance and autoimmune disease prevention [29, 30]. A lot of studies have reported that anomalies in the population or regulatory function of Tregs may be directly linked to the RA, while the reduced number of Tregs had significantly negative correlation with disease activity [31–33]. The current study found that RA patients exhibited a reduced absolute count and percentage of nTregs, and an increased percentage of mTregs compared to HCs. Significantly, RA patients showed an increase in the count of mTregs with escalating disease activity, while the count of nTreg remained unchanged. These findings suggest potential immune dysregulation and pathological roles of Treg subsets, highlighting the need for further investigation into their mechanisms.

This study demonstrated heterogeneity in the distribution of nTreg and mTreg counts among RA patients with varying disease activity. Wang et.al [34] reported that compared to the normal healing group, the proportion of effector mTregs was significantly reduced in patients with tibial fracture. Furthermore, Matsuki et.al revealed that RA patients with elevated disease activity exhibited reduced nTreg counts, which correlated with a significant negative association between nTreg proportions and DAS28 scores [14]. Increasing the frequency and/or suppressive function of Treg subset especially mTregs is a promising approach to overcome the auto-inflammatory reactions in RA. In vivo strategies used to increase the number or function of Tregs include using immunemodulatory drugs such as low dose IL-2, the complex of IL-2/IL-2 receptor, engineered IL-2 muteins and other cytokines [20]. In vitro strategies involve adoptive Treg cell therapy and Chimeric Antigen Receptor (CAR)-Treg cell therapy; process includes isolating Tregs from the peripheral blood or the thymus, then expanding and genetically modifying them in vitro for infusing to the body [20, 35]. CAR-Treg cells derive their suppressive activity in vivo against a desired target from the inclusion of a CD28 domain, which helps maintain their regulatory capabilities [36, 37]. It is evident that the expanded and genetically engineered Tregs are activated mTregs, which possess the capability to rapidly control inflammation and restore immune homeostasis. Owing to their precise modulation in immune responses, CAR-Treg cell therapy present a promising therapeutic strategy for RA.

sIL-2R, a component of the IL-2Rα chain, is widely regarded as a critically serological marker of activated T cells, entering the circulatory pool after immune activation [38, 39]. The concentrations of serum sIL-2R have been reported significantly elevated in patients with RA and systemic lupus erythematosus (SLE) [38]. It has been reported that serum sIL-2R concentrations were regarded as a disease marker of RA and correlated with disease duration, and the decrease in sIL-2R concentration may be a result of joint improvement [40]. Our data are consistent with these findings. Our results demonstrated that the serum sIL-2R concentrations were elevated in RA patients, and positively correlated with DAS28, ESR, CRP and fibrinogen concentrations. In light of this, detection of serum sIL-2R concentrations may further assist in the rapid clinical assessment of disease status in patients with RA.

In our study, IL-2 concentrations were positively correlated with the percentage of Tregs and the absolute count of nTregs. IL-2 signaling mediated through STAT5 is critical for the function, stability, and survival of Tregs [41]. Tregs express high levels of high-affinity trimeric IL-2R, which allows them to respond to very low concentrations of IL-2 [41], whereas other Teffs predominantly display the lower-affinity dimeric IL-2R [42]. Nevertheless, Tregs are incapable of producing IL-2 autonomously and are entirely dependent on an exogenous source of IL-2 [43]. Certain IL-2 muteins or anti-IL-2 antibodies selectively enhance IL-2 signaling in Tregs, leading to the expansion of endogenous Tregs and the suppression of autoimmune responses [44]. Low-dose IL-2 preferentially activates the expansion of endogenous pools of circulating Tregs, which may exert therapeutic effects in numerous inflammatory and autoimmune diseases, including SLE, type I diabetes [41, 45]. A research reported that low-dose IL-2 can significantly restore the expansion of disease-relevant activated mTregs in Type 1 Diabetes Mellitus [46]. Thus, low-dose IL-2 therapy may be promising for RA treatment via mediating the expansion of Treg subset to prevention of disease development.

While our study yields significant insights, it is important to acknowledge several limitations for a thorough interpretation of the results. Firstly, the study focused on quantifying Treg subsets in the peripheral blood of RA patients and did not include an analysis of tissue-resident Treg subsets. Secondly, the heterogeneity in DMARDs regimens among the RA patients could have influenced the counts of Treg subsets. Thirdly, the clinical and serological data were retrospectively collected from a single medical center, which may limit the generalizability of our findings. Lastly, the relatively small sample size of patients suggests the need for a larger cohort to more robustly investigate the prevalence of Treg cell subsets in RA patients and to evaluate their correlation with disease activity.

Conclusion

Patients with RA exhibited a decrease in the number of nTregs, whereas the number of mTregs was similar to that observed in HCs. Furthermore, the count of mTregs in RA patients increased with escalating disease activity, while nTreg counts remained unchanged. IL-2 levels were positively associated with Treg counts, particularly with nTreg counts. Further investigation into targeted therapies that account for the distribution of nTregs and mTregs in RA patients across different levels of disease activity may yield substantially effective approaches for disease remission.

Author Contribution

C.W. and X.Z. designed the article. X.Z. wrote the main manuscript. H.Y. and B.L. participated in the acquisition of data. X.Z., C.G., and X.L. performed statistical analysis and interpretation. C.W., J.L. and H.Y. revised the manuscript. All authors approved the publication of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (No.81971543); Four “Batches” Innovation Project of Invigorating Medical through Science and Technology of Shanxi Province (NO.2022XM05); The Central Guidance Special Funds for Local Science and Technology Development (YDZJSX20231A061); Shanxi Province Higher "Billion Project " Science Technology Guidance Project (BYJL042).

Data Availability

Raw data during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics Approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Second Hospital of Shanxi Medical University [Approval (2019) YX No. (105)].

Conflict of Interest

The authors declare no competing interests.