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Lupus Science & Medicine 2026;13:e002068. doi:10.1136/lupus-2026-002068Integrated immune profiling identifies a coordinated T- cell activation and inflammatory signature in SLEKatarzyna A Lisowska,1 Michał Komorniczak ,2 Martyna Misztal,1 Barbara Bullo- Piontecka ,2 Alicja Dębska-Ślizień,2 Agnieszka Jankowska- Kulawy,3 Anna Wardowska 1To cite: Lisowska KA, Komorniczak M, Misztal M,et al. Integrated immune profiling identifies a coordinated T- cell activation and inflammatory signature in SLE. Lupus Science & Medicine2026;13:e002068. doi:10.1136/ lupus-2026-002068► Additional supplemental material is published online only. To view, please visit the journal online (https:// doi. org/ 10. 1136/lupus- 2026- 002068). Received 25 March 2026 Accepted 1 July 2026 1Department of Rheumatology, Clinical Immunology, Geriatrics and Internal Medicine, Medical University of Gdańsk, Gdańsk, Poland 2Department of Nephrology, Transplantology and Internal Medicine, Medical University of Gdańsk, Gdańsk, Poland 3Division of Laboratory Medicine, Medical University of Gdańsk, Gdańsk, PolandCorrespondence toDr Anna Wardowska; anna.wardowska@ gumed. edu. plImmunology and inflammation© Author(s) (or their employer(s)) 2026. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.ABSTRACTIntroduction Patients with SLE have an increased risk of infections and infection-related mortality, which may be associated with disturbances in immune cell populations. Despite extensive research, findings regarding T-cell and B- cell alterations in SLE remain inconsistent. Therefore, we analysed peripheral blood immune cell subpopulations and their molecular profiles in patients with SLE with renal involvement compared with healthy controls (HCs).Methods The phenotype and gene expression of lymphocytes from patients with SLE and lupus nephritis and healthy donors were assessed using flow cytometry and quantitative PCR. Exploratory multivariate and treatment-sensitivity analyses were performed to evaluate global immune patterns and potential treatment-related confounding.Results Patients with SLE exhibited a decreased proportion of helper T (Th) cells and an increased percentage of cytotoxic T (Tc) cells compared with HC, accompanied by a reduced CD4/CD8 ratio. The percentages of CD4+CD28+ and CD8+CD28+ T cells were lower and correlated with disease duration, whereas the proportions of activated HLA-DR+ Th and Tc cells were increased. Serum interleukin (IL)-6 and IL-10 levels were elevated in patients with SLE. In addition, both T and B cells displayed increased expression of genes associated with inflammatory and interferon-related responses, including TBX21, APOBEC3G, IRF8 and ISG15. Exploratory multivariate profiling suggested coordinated immune activation patterns in SLE, characterised by enhanced T- cell activation, elevated inflammatory cytokines and plasmablast expansion, alongside reduced CD28+ T- cell subsets. Treatment-sensitivity analyses indicated that selected immune parameters may be influenced by glucocorticoid exposure and disease duration.Conclusions Patients with SLE with renal involvement show coordinated disturbances in T-cell and B- cell compartments linked to chronic immune activation, interferon- related responses and disease duration. These findings support the relevance of integrated immune profiling in SLE but should be interpreted as exploratory and require validation in larger longitudinal cohorts.INTRODUCTIONSLE is an autoimmune disease affecting multiple organs in the body. SLE is characterised by diverse severity and natural course; symptoms in one organ may predom- inate for an extended period, with partial remissions and exacerbations. Affected tissues include skin, joints, kidneys, lungs, heart, blood and others. The disease is also accom- panied by systemic symptoms such as fatigue and mild fever.1 As with many other autoimmune diseases, SLE pathogenesis results from overlapping factors that disrupt immune tolerance. Its multifactorial background includes genetic predisposition, environmental factors and sex hormones.2 From an immunological perspec- tive, SLE is a disease of immune complex (IC)- mediated hypersensitivity.1 Its patho- genesis involves three steps: (1) the forma- tion of antigen-antibody complexes, (2) ICWHAT IS ALREADY KNOWN ON THIS TOPIC⇒ SLE is a heterogeneous autoimmune disease driven by immune-complex formation, interferon signalling and dysregulated T-cell and B-cell responses, but findings on immune-cell phenotypes and gene ex- pression remain inconsistent.WHAT THIS STUDY ADDS⇒ This study identifies a coherent pattern of chronic T- cell activation in SLE, characterised by reduced CD28 and increased HLA-DR expression, accompa- nied by elevated plasmablasts, interleukin (IL)-6/IL-10 levels and broad immune and interferon-related gene upregulation.⇒ These alterations correlate with disease duration, in- flammatory markers and cumulative organ damage, and form an integrated immune-activation pattern.HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY⇒ The findings support the development of composite immune biomarkers and reinforce targeting T-cell activation, B-cell responses and interferon path- ways, with potential implications for patient strati- fication in future studies. ### Page 2 Lisowska KA, et al. Lupus Science & Medicine 2026;13:e002068. doi:10.1136/lupus-2026-0020682Lupus Science & Medicinedeposition in vessel walls and (3) complement activation and recruitment of leucocytes by complement products. Activated leucocytes release enzymes and other toxic molecules, which cause tissue inflammation and injury. However, the breakdown of immune tolerance mech- anisms exceeds IC formation and deposition in tissues, leading to overt symptoms. Disease initiation is linked to inadequate clearance of apoptotic cells, leading to the release of nucleopro- teins. These highly immunogenic nuclear antigens are recognised and taken up by antigen-presenting cells (APCs), mainly dendritic cells (DCs).1 DCs are key players in the initiation and perpetuation of abnormal immune responses in SLE. These cells, particularly plasmacy- toid DCs, produce type I interferons (IFNs), which acti- vate lymphocytes.3 High levels of type I IFN result from increased expression of interferon regulatory factors (IRFs).4 The role of conventional DCs in SLE is less clear. It is assumed that repeated stimulation with autoanti- gens can induce their maturation and activation, thus promoting the formation of autoreactive T cells, particu- larly CD4+ T cells (helper T cells, Th cells).5 In SLE, B cells produce pathogenic autoantibodies directed against nucleoproteins, which then form ICs, driving organ inflammation. Studies show disturbances in peripheral B- cell subsets in patients with SLE. Although B cells in SLE have been widely studied, reports on their phenotype remain inconclusive.6–9 Zhu et al8 demon- strated that patients with new-onset SLE had lower percentages of non-switched memory (NSM) B cells and higher percentages of double-negative memory (DNM) B cells. Kosalka et al7 reported lower percentages of NSM B cells and higher switched memory (SM) B cells. Some authors showed that patients with SLE have higher percentages of transitional B cells (TB) and plasmablasts (PBs) as well.9 Regarding T- cell subpopulations, Wang et al10 demon- strated that patients with SLE and lupus nephritis (LN) have lower CD4+ cell percentages and higher CD8+ cell percentages than those with idiopathic membra- nous nephropathy (iMN). According to the authors, CD4+ and CD8+ cells showed the phenotype of effector memory T cells (Tem), which correlated with disease activity. The imbalance between CD4+ and CD8+ cells was also confirmed by other authors.10 Yuan et al11 reported an imbalance in the frequencies of T cells expressing CD28 or HLA-DR antigens. They also demonstrated that patients with SLE have a higher percentage of central memory T cells (Tcm) than healthy individuals. Immune cell dysfunction is, of course, associated with various alterations in gene expression that affect cell differentiation and the cytokine profile in patients with SLE.12 13 These changes in the transcriptome result from epigenetic modifications such as DNA methylation and histone modifications. One of the most important features of SLE, regardless of disease form, is hypometh- ylation of IFN-related genes.14 In CD4+ T cells, 24 genes regulating type I IFN responses have been identified.12 We have previously shown that patients’ B cells exhibit increased expression of several genes, including meth- yl- CpG binding domain protein 2 (MBD2), which play a role in gene silencing by repressing methylated gene promoters.9 Despite many years of research, reports on immune cell populations in SLE remain contradictory and warrant further investigation. Therefore, we attempted to charac- terise SLE-associated alterations in selected immunolog- ical parameters using venous blood from patients with SLE and healthy individuals. We focused on the expression of selected activation markers and memory compartments of different immune cells. Additionally, we performed a detailed molecular analysis of gene expression in isolated T and B cells. We then correlated the results obtained with selected clinical parameters, including disease dura- tion and disease activity, as measured by various scales or inflammatory markers.METHODS Study groupsThe study included 42 women and 2 men with SLE and 15 healthy controls (HCs) (table 1). All patients were treated at the Clinic and Department of Nephrology, Transplan- tology and Internal Medicine, Medical University of Gdańsk, and fulfilled the Systemic Lupus International Collaborating Clinics (SLICC) classification criteria. All patients with SLE included in the study had confirmed LN. Disease activity was assessed using the SLE Disease Activity Index (SLEDAI) and the revised Systemic Lupus Activity Measure (SLAM-R), and damage was assessed using the SLICC/American College of Rheumatology Damage Index (ACR DI); detailed results are presented in table 1. Peripheral venous blood was collected into EDTA tubes for T- cell and B-cell subpopulation analysis and into anticoagulant-free tubes to obtain serum for cytokine measurement. Serum samples were stored at −80°C.Ex vivo analysis of T-cell and B-cell subpopulationsWhole blood (100 μl) was subjected to red blood cell lysis, washed with phosphate-buffered saline (PBS) and stained with fluorochrome-conjugated monoclonal antibodies (BD Pharmingen, BioLegend) targeting T-cell and B- cell markers, including CD3, CD4, CD8, CD19, CD24, CD27, CD28, CD38, CD45RA, CD69, CD127, CD197 (CCR7), HLA- DR and immunoglobulin D (IgD). Staining was performed for 30 min at room temperature in the dark. After washing, cells were resuspended in PBS and analysed using a FACSVerse cytometer (Becton Dickinson).Cytokine measurementSerum concentrations of interleukin (IL)-2, IL-4, IL-6, IL- 10, tumour necrosis factor (TNF), IFN-γ and IL-17A were determined using the Cytometric Bead Array Human Th1/Th2/Th17 Cytokines Kit (BD Biosciences) according to the manufacturer’s protocol. Quantitative fluorescence analysis was performed on a FACSAria III ### Page 3 Lisowska KA, et al. Lupus Science & Medicine 2026;13:e002068. doi:10.1136/lupus-2026-002068 3Immunology and inflammationcytometer (Bection Dickinson). The detection range for all cytokines was 20–5000 pg/mL.T-cell and B-cell isolationPeripheral blood mononuclear cells were isolated by density- gradient centrifugation using Histopaque-1077 (Sigma Aldrich). CD19-positive B cells were separated magnetically using Dynabead CD19 Pan B. The remaining CD19- negative fraction was used to isolate CD3-positive T cells with Dynabead CD3 (Thermo Fisher Scientific). All procedures followed manufacturer protocols. Isolated cells were stored at −80°C for molecular analysis.Molecular analysisTotal RNA was extracted using the GeneMATRIX Universal DNA/RNA/Purification Kit (EurX). RNA quality and concentration were assessed spectrophotometrically (Epoch, Bio- Tek). Reverse transcription was performed with the NG dART RT Kit (EurX). Expression of 20 T- cell and 10 B-cell transcripts was analysed using TaqMan Gene Expression Assays (Thermo Fisher Scientific). Real-time PCR reactions (10 μl) were carried out with Maxima Probe qPCR Master Mix on a PikoReal Real-Time PCR System (Thermo Fisher Scientific). Cycling conditions included 50°C for 2 min, 95°C for 10 min, followed by 40 cycles of 95°C for 10 s and 60°C for 60 s. Relative gene expression was calculated using the comparative Ct (ΔCt) method and normalised to GAPDH. Primer and probe details are provided in online supplemental table 1.Flow cytometric and statistical analysisCytometric data were analysed with FlowJo V.10 (Becton Dickinson). Lymphocytes were gated based on Forward Scatter (FSC) and Side Scatter (SSC) parameters. T cells were identified as CD3+, B cells as CD19+. Within T cells, CD4+ helper (Th) cells and CD8+ cytotoxic (Tc) cells were defined. Expression of CD28, CD69 and HLA-DR was assessed within CD4+ and CD8+ subsets. Based on CD197 (CCR7) and CD45RA expression, T- cell subsets were classified as naïve (CD197+CD45RA+), Tcm (CD197+CD45RA−), Tem (CD197−CD45RA−) and effector memory re-expressing (Temra; CD197− CD45RA+). Regulatory T cells (Tregs) were defined as CD4+CD127−CD25+. In B cells, CD24 and CD38 expression identified TB (CD24++CD38++), and PBs (CD24−CD38++C- D27+IgD−). Among CD38− B cells, DNM (CD27−IgD−), SM (CD27+IgD−), NSM (CD27+IgD+) and naïve B cells (CD27−IgD+) were distinguished. Representative gating strategies are provided in the Supplementary Materials (online supplemental figure 1). Statistical analyses were performed using GraphPad Prism V.9. Data distribution was evaluated with Kolmogorov-Smirnov and Shapiro-Wilk tests. A p value <0.05 was considered statistically significant.Multivariate and exploratory treatment-sensitivity analysesMultivariate analysis was performed to evaluate global patterns of immune dysregulation in patients with SLE compared with HCs. Principal component analysis (PCA) was conducted using selected immunological variables, including the CD4/CD8 ratio, CD8+HLA-DR+ T cells, CD8+CD28+ T cells, CD4+CD28+ T cells, Treg, PBs, IL-6 and IL-10. Prior to analysis, all variables were standardised by applying a z- score transformation to ensure compa- rability across different measurement scales. PCA was performed based on singular value decomposition, and the contribution of each variable to the principal compo- nents was assessed using loading coefficients. To visualise sample similarity and marker expression patterns, hier- archical clustering was performed using Euclidean distance and complete linkage on the same standardised dataset. Results were presented as a heatmap with row-wise scaling. PCA and hierarchical clustering were used as exploratory unsupervised approaches to assess global immune patterns.Table 1 Comparison of basic clinical parameters of patients with SLE and healthy peopleHC (n=15) SLE (n=44)Age (years) 40.13±9.22 46.18±12.05 Woman:man 15:0 42:2 LN duration (years) n.a. 16.5 (2–42) SLICC/ACR DI (score) n.a. 1 (0–6) SLEDAI (score) n.a. 6 (0–26) SLAM- R (score) n.a. 5 (1–17) Symptoms (organ involvement) n.a.Joints 41 (93.18%)Skin 37 (84.09%)Neurological 8 (18.18%)Haematological 36 (81.82%)Renal 44 (100%)Serous membranes 13 (29.55%) Treatment n.a.Glucocorticoids 39 (88.64%)Immunosuppressants 32 (72.73%) ESR (mm/hour) n.a. 12 (0–42) CRP (mg/L) n.a. 1.24 (0–35.74) eGFR (mL/min/1.73 m2) n.a. 86 (13–90) Creatinine (mg/dL) n.a. 0.83 (0.53–4.17) Anti- dsDNA (IU/mL) n.a. 184.58 (0–800) anti-dsDNA, anti- double- stranded DNA antibodies; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; ESR, erythrocyte sedimentation rate; HC, healthy control; LN, lupus nephritis; n.a., not applicable; SLAM-R, Systemic Lupus Activity Measure-revised; SLEDAI, SLE activity was assessed using the SLE Disease Activity Index; SLICC/ACR DI, Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index. ### Page 4 Lisowska KA, et al. Lupus Science & Medicine 2026;13:e002068. doi:10.1136/lupus-2026-0020684Lupus Science & MedicineExploratory treatment-sensitivity analyses were performed using multivariable linear regression. Selected cytometric outcomes and the integrated cytometry first principal component score (PC1 score) were modelled with LN/SLE status, age, glucocorticoid (GC) dose and mycophenolate mofetil (MMF) exposure as covariates. Additional SLE- restricted models included age, SLE duration, SLEDAI, GC dose and MMF exposure. Stan- dardised β coefficients were reported, with Benjamini-Hochberg false discovery rate correction. All analyses were performed using R Statistical Software (V.4.5.3; R Core Team 2026).RESULTS Characteristics of patients with SLE42 women and 2 men were enrolled in the study (table 1). The mean age in the SLE group was 46.18±12.05, while in HCs, 40.13±9.22. Renal involvement was present in all patients with SLE included in the study, while 93.18% of the included patients reported joint pains. Other common manifestations of the disease included cuta- neous (84.09%) and haematological (81.82%) symp- toms. The median SLEDAI score was 6, depicting good management of disease activity. Regarding treatment, 88.64% of patients received GCs and 72.73% received immunosuppressants. Detailed information on treatment background, including the use of GC and immunosup- pressive agents, is provided in online supplemental table 2. None of the patients had previously received B-cell-depleting therapy, such as rituximab or obinutuzumab, or belimumab; cyclophosphamide had been discontinued more than 6 months before enrolment.Alterations in T-cell and B-cell subpopulations in lupusThe analysis of the general population of T cells revealed no significant differences in the percentage of CD3+ cells between patients with SLE and HC. Concurrently, patients showed a decreased percentage of CD3+CD4+ (Th) cells (figure 1A) and a lower CD4+/CD8+ratio (figure 1A) compared with HC. This reduction in Th cells was accom- panied by an increase in the percentage of CD3+CD8+ (Tc) cells (figure 1A). Interesting alterations were detected within CD4+ and CD8+ T cell compartments. The SLE group showed lower percentages of CD28+Th cells and Tc cells (figure 1A). Both Th and Tc CD28-positive subpopulations were significantly reduced in patients with SLE, whereas consequently, the percentages of CD28-negative populations increased (figure 1B,C). Additionally, HLA-DR, a late marker of T cell activation, was expressed in a higher percentage of CD4+T cells and CD8+T cells in SLE compared with HC (figure 1A). However, two groups of HLA-DR- expressing cells could be distinguished within the Th and Tc cell populations. Both CD28-positive and CD28- negative subpopulations of CD4+ and CD8+ T cells showed a higher percentage of HLA- DR- expressing cells (figure 1B,C). The percentage of Treg cells (CD4+CD127−CD25+) (figure 1A) was significantly higher in patients than in healthy individ- uals. No significant differences were observed in CD69-positive cell percentages in either T cell subpopulation (online supplemental table 3). Regarding memory compartments of T cells, no signif- icant shifts were detected in the analysed T cell subpop- ulations (online supplemental table 3). Nevertheless, promising trends have been observed within CD8+ T cell memory subsets. Among Tc cells, the percentages of naïve (CD197+CD45RA+), Tcm (CD197+CD45RA−) and Tem (CD197−CD45RA−) presented a declining tendency in patients with SLE. On the other hand, the patient group showed an increased percentage of Temra cells (CD197−CD45RA+), in both CD4+ and CD8+ memory T cell compartments. The percentage of CD19+ B cells in patients with SLE was lower than in HC, but the statistical significance was not reached. Other noted changes in TB (CD24++CD38++), naïve (CD38−CD27−IgD+), DNM (CD38−CD27−IgD−), NSM (CD38−CD27+IgD+) or SM (CD38−CD27+IgD−) B cells (online supplemental table 2) were not meaningful. However, the SLE group had a higher percentage of PB (CD24−CD38++CD27+IgD−) than HC (figure 1A).Increased levels of IL-6 and IL-10 in patients’ seraTo assess the differences in the secretory profile of the SLE cohort and healthy individuals, the analysis of Th1/ Th2/Th17 cytokines was performed. Two out of seven tested cytokines showed a different pattern of serum levels in patients, precisely IL-6 and IL-10 concentrations were significantly higher in the SLE group compared with HC (figure 2A). No differences were detected in the levels of other analysed cytokines, IFN-γ, IL-2, IL- 4, IL- 17A or TNF between study groups (figure 2A).Gene expression pattern is altered in patients with SLET cells from patients with SLE showed an altered gene expression pattern compared with the HC group (online supplemental table 4). Statistically significant increases were noted for master transcription factors for Th1, Th2, Th17 and Treg cells: TBX21, GATA3, RORC and FoxP3, respectively (figure 2B). Other upregulated genes in the patient group included TRIM21, APOBEC3G, IRF8, ISG15, IFNG and IL12 (figure 2B). Several genes were undetectable in both groups (eg, IL2, IL1A, IL17). Regarding gene expression in B cells, three genes proved to be upregulated in the SLE group: ISG15, BAFF and IL10 (figure 2C). At the same time, expression of the IL6 gene in B cells was decreased compared with HC (figure 2C). No statistically significant differences were detected in the expression of APOBEC3G, IRF8, TGFB, IL12 or TNF between the examined groups (online supplemental table 4).Correlations between immunological parameters and clinical characteristicsSeveral significant correlations between acquired data and clinical characteristics were identified in the SLE ### Page 5 Lisowska KA, et al. Lupus Science & Medicine 2026;13:e002068. doi:10.1136/lupus-2026-002068 5Immunology and inflammationFigure 1 Alterations in T-cell subpopulations, CD28 expression and activation status in SLE. (A) Comparison of major T-cell subpopulations between HC and patients with SLE, including CD3+CD4+ (Th) and CD3+CD8+ (Tc) cells, CD4/CD8 ratio, CD4+CD28+ T cells, activated CD4+HLA- DR+ T cells, Tregs and PBs. (B) Distribution of CD4+ T- cell subsets defined by CD28 expression (CD28+ vs CD28−) (upper panel) and the proportion of HLA-DR+ cells within CD4+CD28+ and CD4+CD28− subsets (lower panel) in HC and patients with SLE. (C) Distribution of CD8+ T- cell subsets defined by CD28 expression (CD28+ vs CD28−) (upper panel) and the proportion of HLA-DR+ cells within CD8+CD28+ and CD8+CD28− subsets (lower panel) in HC and patients with SLE. Data are presented as median with 95% CIs. Each dot represents an individual subject. Statistical significance was determined using the Mann- Whitney U test. p<0.05, *p<0.01, **p<0.001, ***p<0.0001. HC, healthy control; PB, plasmablast; Tregs, regulatory T cells. ### Page 6 Lisowska KA, et al. Lupus Science & Medicine 2026;13:e002068. doi:10.1136/lupus-2026-0020686Lupus Science & MedicineFigure 2 Serum cytokine profile and gene expression signatures of immune cells in SLE. (A) Serum concentrations of IL-6 and IL- 10 in HC and patients with SLE. The accompanying table summarises additional cytokines (IFN-γ, IL- 2, IL- 4, IL- 6, IL- 10, IL-17A, TNF), presented as median (range), along with corresponding p values; statistically significant results (p<0.05) are highlighted in bold. (B) Relative expression (RFU) of T-cell- associated transcription factors and immune-related genes, including TBX21, GATA3, RORC, FOXP3, TRIM21, APOBEC3G, IRF8, ISG15, IFNG and IL12, in HC and patients with SLE. (C) Relative expression (RFU) of selected B-cell-related and cytokine-associated genes, including ISG15, BAFF, IL6 and IL10, in HC and patients with SLE. Data are presented as median with 95% CIs. Each dot represents an individual subject. Statistical significance was determined using the Mann-Whitney U test. p<0.05, *p<0.01, **p<0.001, ***p<0.0001. HC, healthy control; IFN-γ, interferon-gamma; IL, interleukin; RFU, relative fluorescence unit; TNF, tumour necrosis factor. ### Page 7 Lisowska KA, et al. Lupus Science & Medicine 2026;13:e002068. doi:10.1136/lupus-2026-002068 7Immunology and inflammationgroup. The percentage of CD8+CD28+ cells was negatively correlated (figure 3A), while the percentage of CD8+H- LA- DR+ cells was positively correlated with SLE duration. Another negative correlation was observed between the percentage of CD4+CD28+ cells and LN duration. More- over, the same population of T cells (CD4+CD28+) along with CD8+CD28+ T cells showed a negative correlation with the SLICC/ACR DI. The percentage of CD8+CD28+ cells was negatively correlated with C-reactive protein (CRP), whereas the ratio of CD8+HLA-DR+ cells was posi- tively associated with CRP and the SLICC/ACR DI. The observed specific pattern of T-cell-related gene expression in the SLE group also correlated with several clinical features (figure 3B). Expression of the TBX21 gene was positively correlated with SLE and LN duration, and with the SLICC score, whereas expression of the GATA3 gene was negatively correlated with the SLAM- R score. FOXP3 gene expression was positively correlated with serum creatinine (Cr). IRF8 gene expression was negatively correlated with LN duration and SLICC/ ACR DI. Expression of the ISG15 gene was positively correlated with ESR and eGFR, and negatively with serum Cr. Expression of APOBEC3G and IL12 was positively correlated with the SLICC/ACR DI score.Exploratory multivariate profiling suggests coordinated immune activation patterns in SLEPCA demonstrated partial separation between patients with SLE and HC (figure 4A). The first principal compo- nent (PC1), explaining 35.4% of the total variance, represented the main axis of immune variation and distinguished SLE cohort from controls, while the second component (PC2), explaining 24.3% of the variance, reflected additional heterogeneity within the cohort. Loadings analysis revealed that PC1 was primarily driven by increased CD8+HLA-DR+ T cells, IL- 6, IL-10 and PBs, which were associated with patients with SLE, whereas CD8+CD28+ T cells, CD4+CD28+ T cells and the CD4/ CD8 ratio contributed in the opposite direction and were associated with HC (figure 4B). Hierarchical clus- tering further supported these findings, showing partially distinct clustering trends with overlap between SLE and HC groups (figure 4C). Patients with SLE displayed greater dispersion compared with controls, indicating increased heterogeneity of immune profiles. The heatmap revealed a coordinated pattern characterised by elevated T-cell activation markers, increased inflammatory cytokine levels and elevated PB levels in SLE, suggesting a pattern consistent with systemic immune activation, although these exploratory findings require external validation.Exploratory treatment-sensitivity analysesTo address potential treatment-related confounding, we performed exploratory multivariable sensitivity analyses (online supplemental table 5). First, models including all participants were fitted to selected cytometric outcomes, with SLE status, age, GC dose and MMF exposure as covariates. In these models, the SLE group effect did not remain significant after false-discovery-rate correction for any of the analysed cytometric parameters. In contrast, GC dose was associated with selected immune parame- ters, including lower CD3+CD4+, CD4+CD28+, CD8+CD28+ and CD19+ frequencies, higher CD8+HLA- DR+ frequency and a higher integrated cytometry PC1 score. Because treatment exposure was structurally linked to SLE status, we additionally performed models restricted to the SLE cohort (online supplemental table 6). In these analyses, SLE duration remained associated with CD3+CD4+, CD3+CD8+, CD4/CD8 ratio, CD8+CD28+ cells and the integrated cytometry PC1 score. GC dose remained associated with CD4+HLA- DR+ and CD8+H- LA- DR+ cells, as well as with the PC1 score. MMF expo- sure, including the predefined ≥1000 mg/day threshold, was not significantly associated with the analysed parame- ters after correction for multiple testing. Similar exploratory analyses were performed for selected transcriptomic and cytokine parameters (online supplemental table 7). In unadjusted comparisons, patients with SLE showed higher expression of selected T- cell transcriptomic markers, including T-cell TBX21/ T- bet, TRIM21, APOBEC3G, IRF8, ISG15 and RORC, as well as higher serum IL-6 and IL- 10 concentrations compared with HCs after false discovery rate correc- tion. In all-participant models adjusted for age, GC dose and MMF exposure, SLE status was not retained after false discovery rate correction for any transcriptomic or cytokine parameter. In SLE-restricted models, SLE duration remained associated with T-cell TBX21/T-bet expression, whereas GC dose remained associated with T- cell APOBEC3G expression. MMF exposure was not significantly associated with transcriptomic or cytokine outcomes after false discovery rate correction. Overall, these findings indicate that treatment expo- sure, particularly GC dose, contributes to variability in the observed immune phenotype, while disease duration also remains associated with selected T-cell alterations within the SLE cohort.DISCUSSIONHeterogeneity of SLE clinical manifestations is visibly reflected by inconsistencies in the reported patterns of immunological parameters characteristic. In this paper, we attempted to identify clear alterations in immuno- logical components and to verify our observations using available literature. Therefore, we compared subpop- ulations of T and B cells, gene expression patterns and serum cytokine profiles between patients with SLE and HCs. Because all patients included in the present cohort had confirmed renal involvement, the identified immune alterations should primarily be interpreted in the context of SLE with LN rather than the full clinical spectrum of SLE. Further stratification by LN activity may help clarify the biological and translational relevance of these immune alterations. The simplicity of the analysed immu- nological panels was deliberate to highlight the usefulness ### Page 8 Lisowska KA, et al. Lupus Science & Medicine 2026;13:e002068. doi:10.1136/lupus-2026-0020688Lupus Science & MedicineFigure 3 Correlation analysis of clinical parameters, immune cell subsets and gene expression profiles in SLE. (A) Correlation matrix showing relationships between clinical parameters (SLE duration, LN duration, SLEDAI, SLAM-R, SLICC/ACR DI, ESR, CRP, Cr, eGFR) and T-cell subpopulations, including CD3+CD4+, CD3+CD8+, CD4+CD8+, CD4+CD28+, CD8+CD28+, CD4+HLA-DR+, CD8+HLA- DR+ T cells, Tregs and PBs. (B) Correlation matrix showing relationships between clinical parameters and gene expression levels (RFU) of immune-related genes, including TBX21, RORC, GATA3, FOXP3, TRIM21, APOBEC3G, IRF8, ISG15 and IL12. Correlation coefficients are presented as Spearman’s rank correlation values. Colour scale represents the strength and direction of correlations (blue, positive; red, negative). Values within each cell indicate correlation coefficients (r). Cr, creatinine; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; ESR, erythrocyte sedimentation rate; IL, interleukin; LN, lupus nephritis; PB, plasmablast; RFU, relative fluorescence unit; SLAM-R, Systemic Lupus Activity Measure-revised; SLEDAI, SLE Disease Activity Index; SLICC/ACR DI, Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index; Tregs, regulatory T cells. ### Page 9 Lisowska KA, et al. Lupus Science & Medicine 2026;13:e002068. doi:10.1136/lupus-2026-002068 9Immunology and inflammationand versatility of venous blood as a basis for diagnosing and monitoring patients with SLE. The exploratory use of PCA and hierarchical clustering enabled visualisation of integrated immune patterns rather than isolated biomarkers, providing a more comprehensive view of immune dysregulation in SLE. However, these analyses were not designed as predictive classification tools, and the identified multidimensional patterns require valida- tion in larger independent cohorts. At the same time, these findings should be interpreted in the context of potential clinical confounders, including treatment expo- sure, disease duration, disease activity and predominant renal involvement in the study cohort. The fundamental alteration in the T-cell subset was visible as a disturbance of the CD4+/CD8+ ratio in the SLE group. This effect was associated with a significant reduction in CD4+ cells in the SLE group. Wang et al,15 who compared T-cell subsets between LN and iMN (idio- pathic membranous nephropathy) patients, reported similar results. Also, Yang et al10 described a CD4+/CD8+ imbalance in T-cell subsets between patients with SLE and healthy individuals. Interesting observations were made in the CD4+ and CD8+ T cell compartments, revealing significant changes in CD28 expression. Both Th and Tc cells from patients with SLE showed a clear imbalance inFigure 4 Exploratory PCA and hierarchical clustering suggest coordinated immune activation patterns in SLE. (A) PCA score plot of HC and SLE samples based on selected immunological markers. PC1 and PC2 explained 35.4% and 24.3% of the variance, respectively. (B) PCA loading plot showing the variables driving sample separation. CD8+HLA-DR+ T cells, IL- 6, IL-10 and plasmablasts were associated with the SLE direction, whereas CD8+CD28+ T cells, CD4+CD28+ T cells and the CD4/ CD8 ratio were associated with HC. (C) Heatmap with hierarchical clustering of z-score-standardised immunological variables across individual subjects. Red indicates relatively higher and blue relatively lower values. These analyses suggest an immune activation profile in SLE marked by enhanced T-cell activation, inflammatory cytokine expression and B-cell differentiation. HC, healthy control; IL, interleukin; PB, plasmablast; PC, principal component; PCA, principal component analysis; Treg, regulatory T cell. ### Page 10 Lisowska KA, et al. Lupus Science & Medicine 2026;13:e002068. doi:10.1136/lupus-2026-00206810Lupus Science & MedicineCD28+ and CD28− cells, with a dominance of the CD28-negative subpopulation. Moreover, the percentage of cells expressing CD28 was reduced with increasing disease duration, but not with disease activity, as in other reports.16 CD28 is an important costimulatory molecule that delivers a second signal to activated T cells and influ- ences their proliferative capacity and cytokine produc- tion.17 The accumulation of CD28- deprived T cells is associated with age and the acquisition of regulatory functions by these cells.18 On the other hand, Yu et al19 speculated that the reduced CD28 expression observed in their SLE cohort could be linked to T-cell exhaustion, which is typically associated with cancer rather than auto- immune diseases. The authors suggested that multiple rounds of activation and proliferation may result in the formation of CD28- negative effector cells. This phenom- enon was described in chronic inflammation and auto- immune diseases.19 Our data showing an increased percentage of CD8+CD28 T cells is in line with previous reports16; however, it requires further analysis of exhaus- tion markers. CD4+ T cells with decreased expression of CD28 are described in the literature as CD4+ Tc cells and believed to play a pathogenic role in SLE development by promoting target cell death.20 21 CD4+ and CD8+ T cells from patients with SLE also showed increased HLA-DR expression. The positive correlation between CD8+HLA- DR+ cells and disease duration may suggest persistent activation of Tc cells over the disease course. Similar changes in T cells have already been described in patients with SLE, particularly those with renal involvement.10 11 HLA-DR is a member of the major histocompatibility complex II present mainly in APCs. However, it can also be found on the surface of activated Th and Tc cells, suggesting that patients with SLE exhibit an abnormally overactivated immune status. Previous studies have linked CD28 loss with chronic anti- genic stimulation and autoimmune inflammation,16 22 23 while increased HLA-DR expression has been reported as a marker of T-cell activation in SLE and LN.11 24 Therefore, the combined pattern of reduced CD28 and increased HLA- DR observed in our cohort may reflect chronic immune activation, although additional markers are required to distinguish activation from exhaustion, senescence or terminal differentiation. Exploratory treatment- sensitivity analyses further suggested that some cytometric alterations may be influenced by GC exposure and disease duration, highlighting the need for cautious interpretation of cross- sectional immune profiling data. Why activated T cells express HLA-DR remains unclear. Studies show that they acquire HLA-DR expression in response to antigen exposure, for example, Mycobacte- rium tuberculosis.25 However, in T cells deprived of CD28 expression, the presence of HLA-DR together with high expression of IFN-γ, IL-4 and IL-10 can be a marker of highly differentiated primed-memory T cells.26 Increased percentages of HLA-DR- positive cells were observed in both CD28-positive and CD28-negative Th and Tc popu- lations, suggesting distinct characteristics of these cells. Double- positive T cells (CD28+HLA-DR+) are considered specialised effector T cells, whereas double-negative T cells represent highly differentiated or exhaustion-like cells.27 Therefore, further validation of our observations seems inevitable. Interestingly, in our study, patients with SLE had a higher proportion of Tregs (defined as CD4+CD127− CD25+) than healthy people. Moreover, it was associated with increased FoxP3 gene expression. According to a recent meta- analysis by Li et al,28 the pooled percentages of Tregs in active SLE are lower than in HC, but with great heterogeneity. Some studies showed that patients with SLE had lower percentages of Tregs, defined as CD4+CD25+- FoxP3+ cells,29 30 while others demonstrated increased Tregs but with dysfunctional activity.31 32 There seem to be many reasons for the discrepancy in the published results, including Treg phenotyping and gating, or diag- nostic SLE criteria. Li et al28 noted that the choice of Treg marker is essential: the percentage of Tregs was signifi- cantly lower in patients with active SLE when CD25 was used as a marker, whereas no difference was observed when Tregs were distinguished by FoxP3 or CD127 expres- sion. In the present study, Tregs were identified based on CD4+CD25+CD127− surface marker expression, which represents a commonly used but imperfect surrogate for functional Tregs. The absence of FoxP3 protein confir- mation, epigenetic validation, such as TSDR analysis, and functional suppression assays limits definitive interpreta- tion of the regulatory capacity of this population. Some authors associate changes in the percentage of Tregs with disease status.30 33 We assume that the increase in Treg percentage and the upregulation of FoxP3 in our study group were not accompanied by preserved regulatory function of these cells. Various scenarios explaining the loss of regulatory properties of these cells are plausible and require further investigation. First, epigenetic studies evaluating post-transcriptional modifications of the FoxP3 transcript and microRNA targeting this sequence could be useful. On the other hand, Tregs are likely to differen- tiate into Th17 cells in highly inflammatory conditions, as observed in numerous autoimmune diseases.34 Molecular analysis revealed several changes in T-cell gene expression. We focused on the genes involved in T- cell differentiation and cytokine patterns. Based on the upregulation of selected transcription factors that promote T-cell differentiation into various subpopu- lations, we may suggest that specific T-cell subsets are prioritised in lupus. T cells from patients with SLE had increased expression of TBX21 encoding T-bet, which is specific for the Th1 subpopulation, GATA3 specific for the Th2 subpopulation, RORC encoding RORγt, specific for the Th17 subpopulation and a marker of Tregs— FOXP3. Also, there was an increase in the expression of TRIM21, which is associated with the intracellular antibody-mediated proteolysis pathway, and APOBEC3G, which is involved in innate antiretroviral immune activity against retroviruses. It was not surprising that T cells from the SLE group showed high IRF8 expression, a regulator ### Page 11 Lisowska KA, et al. Lupus Science & Medicine 2026;13:e002068. doi:10.1136/lupus-2026-002068 11Immunology and inflammationof type I IFN expression and ISG15, a protein induced by type I IFN that acts as a cytokine and an intracellular protein modifier. However, because the transcript analysis was based on a targeted gene panel, these findings should be interpreted as suggestive of IFN-related pathway involvement rather than definitive evidence of a global IFN- driven transcriptional programme. Future studies incorporating broader ISG panels, validated IFN scores or unbiased transcriptomic approaches are needed to better define IFN- driven responses in SLE. Also, genes coding IFN-γ and IL-12 were upregulated in T cells. These genetic changes suggest that T cells from patients with SLE are dysregulated and may be polarised toward inflammatory responses and B- cell class switching.13 For transcriptomic and cytokine outcomes, exploratory treatment-sensitivity analyses indicated that selected signals were associated with disease duration or GC dose, while MMF exposure was not retained after false discovery rate correction. Therefore, the serum cytokine and transcriptional find- ings should be interpreted in the context of treatment exposure and disease duration. Our further observations on the distribution of Th subpopulations (Th1, Th2, Th17 and Treg) in lupus confirm notable alterations in the Th17/Treg balance. As reported in molecular analyses, both RORC and FoxP3 were upregulated in T cells from patients with SLE. At the cellular level, we confirmed increased percentages of Treg cells, with plausible dysfunctional activity reflected in the presence, duration and intensity of the autoim- mune process. The presence and activity of Th17 subsets were indirectly assessed by measuring IL-17 transcript and IL-17 protein levels in patient sera. To our surprise, IL-17 was detected only at the protein level, with no signif- icant differences in serum concentration between groups. Simultaneously, messenger RNA for IL-17 was undetect- able in any of the tested groups. The discrepancy between serum IL-17A levels and undetectable IL17 transcripts in T cells may reflect alternative cellular sources, low basal transcript expression or methodological sensitivity limita- tions. Future studies incorporating stimulation assays and intracellular cytokine profiling are needed to clarify the origin of IL-17A production in SLE. According to the literature, proinflammatory cytokines may inhibit FoxP3 function, thus promoting Th17 differentiation. It is prob- able that increased levels of IL-6 in our SLE group could affect the action of FoxP3, leading to a high number of dysfunctional Tregs prone to transform into Th17 cells. The low levels of IL-17 itself and its transcript, observed in our study, require deeper insight, epigenetic analyses and complex functional tests. In B-cell subpopulations, the change was observed only in PB proportions, which were higher in patients with SLE. Increased peripheral PBs, especially producing Ig, have been confirmed by several authors.35 36 In SLE, these cells produce pathogenic autoantibodies against DNA and other nuclear antigens. Some studies demonstrated that autoantibodies can be generated from self-reactive or non- self- reactive cells.37 One of the most well-known biological drugs used to treat SLE is rituximab, which recognises cells that express the CD20 antigen. The problem is that CD20 expression diminishes in late PBs, eventually being lost in well-differentiated plasma cells responsible for Ig production. While initially rituximab depletes B cells, residual circulating B cells, especially IgA- positive PBs, can be detected.38 According to some studies, these PBs are responsible for the plasmocytosis observed in patients with SLE.36 Molecular analysis of B cells confirmed that these cells, similar to SLE T cells, have increased expression of ISG15, a protein induced by type I IFN. They also exhibit increased expression of the BAFF gene, which is essen- tial for B-cell survival, maturation and activation. BAFF can be produced by a variety of cell types, especially those involved in antigen presentation, such as macrophages, monocytes, DCs39 and T cells.40 Chu et al41 demonstrated that BAFF is expressed at every stage of B-cell differentia- tion. Moreover, authors have shown that high expression of BAFF in B cells is associated with their activation, which may be important in the course of autoimmune diseases such as lupus.42 The expression of IL6 in B cells of the SLE group was decreased, suggesting that some other cells are respon- sible for the high serum IL-6 observed in this group. Its high levels have been previously associated with SLE activity43 and inflammatory markers.44 According to some studies, IL-6 is responsible for excessive B-cell differentia- tion in patients with SLE.45 46Limitations of the studyThe presented study is limited by a relatively small sample size, particularly in the control group, a single-centre design and the racial and ethnic homogeneity of enrolled patients. These factors may have affected statistical power, especially for multivariate models and subgroup analyses, and may also limit the generalisability, interpretation and broader applicability of the findings. The single-centre, cross-sectional design also limits causal inference and does not allow evaluation of temporal changes in immune parameters over the course of disease activity, remission or treatment response. Future longitudinal multicentre studies are needed to validate the clinical relevance and temporal stability of the identified immu- nological patterns, including clinically stratified analyses comparing active and inactive LN to determine whether the observed immune alterations differ according to renal disease activity. In addition, PCA and hierarchical clustering were performed as exploratory analyses to assess global immune patterns and were not designed as predictive classification tools. Accordingly, the absence of external validation, stability testing and formal predictive performance metrics limits the clinical applicability of these findings. The interpretation of immune findings is further limited by potential clinical confounders, including treatment exposure, disease activity, disease duration, organ involvement and clinical heterogeneity of SLE ### Page 12 Lisowska KA, et al. Lupus Science & Medicine 2026;13:e002068. doi:10.1136/lupus-2026-00206812Lupus Science & Medicinemanifestations. Because most patients received GCs and/ or immunosuppressive therapy, treatment-related effects on immune-cell phenotypes, cytokine levels, PB frequen- cies and gene- expression profiles cannot be excluded. Although exploratory multivariable sensitivity analyses were performed, GC dose was associated with selected activation- related immune parameters, particularly HLA-DR expression and the integrated cytometry PC1 score. Therefore, the observed CD28/HLA-DR- related profile should be interpreted in the context of a predomi- nantly treated SLE cohort. The cross-sectional design and the structural association between SLE status and treat- ment exposure preclude definitive separation of disease-related and treatment- related effects. Therefore, the observed associations should be considered exploratory and hypothesis-generating rather than definitive. Nevertheless, the homogeneity of the patient group in terms of SLE manifestation, namely LN, and sex, as 95% of patients with SLE were women, appears to be a major strength of our investigation. Importantly, despite the modest cohort size, several findings reached statistical significance. Therefore, the current results should be considered preliminary and require validation in larger, independent, multicentre cohorts encompassing patients with diverse clinical manifestations of SLE.CONCLUSIONSOur results show that patients with SLE exhibit signif- icant changes in T- cell and B-cell phenotypes and gene expression. Patients with SLE primarily have disturbances in the proportions between Th and Tc cells, which seem to be a characteristic feature of this disease. Their T cells are also characterised by reduced CD28 expression and increased HLA-DR expression, consistent with chronic immune activation in the course of SLE. These features appear to be related to disease duration and selected clinical parameters. Our results also confirm previously reported observations of elevated serum IL-6 and IL-10 levels and suggest altered expression of selected immune-related and IFN-related genes. Together, these findings indicate coordinated immune alterations in patients with SLE with renal involvement, although they should be interpreted as exploratory and require validation in larger longitudinal cohorts.Contributors KAL: Conceptualisation, Methodology, Formal Analysis, Investigation, Data Curation, Writing—Original Draft Preparation, Visualisation, Supervision. MK: Investigation, Data Curation, Formal Analysis. MM: Investigation. BBP: Investigation. ADŚ: Writing—Review and Editing. AJK: Writing—Review and Editing. AW: Conceptualisation, Methodology, Formal Analysis, Investigation, Resources, Visualisation, Writing—Review and Editing, Supervision, Project Administration, Funding Acquisition, Guarantor.Funding This work was supported by the Polpharma Scientific Foundation, grant no. 1/XX/2021 (awarded to AW).Competing interests None declared.Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.Patient consent for publication Not applicable.Ethics approval This study involves human participants and was approved by the Independent Bioethics Committee for Scientific Research (NKBBN/506-255/2022) and conducted in accordance with relevant guidelines. Participants gave informed consent to participate in the study before taking part.Provenance and peer review Not commissioned; externally peer reviewed.Data availability statement Data are available upon reasonable request.Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer- reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.Open access This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. 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