Dendritic cells (DCs) play a central role in the immunopathogenesis of rheumatoid arthritis (RA), yet their regulation by tumor necrosis factor alpha (TNF) and associated receptors remains poorly characterized. We applied a single-cell multi-omics approach (CITE-seq) to profile peripheral blood mononuclear cells (PBMCs) from RA patients and healthy donors, before and after in vitro TNF stimulation. Using integrated analysis of surface protein expression and transcriptomic data, we focused on phenotypic and transcriptional changes in dendritic cell populations. DCs from RA patients exhibited elevated surface expression of CD14 and CD16, indicative of an inflammatory phenotype, and showed marked responsiveness to TNF. Upon stimulation, RA-derived DCs upregulated genes involved in antigen presentation (CD83, LAMP3), lymph node migration (CCR7, ADAM19), and inflammation (TRAF1, IL24) whereas such activation was absent in healthy controls. Our data reveal a TNF-responsive, pro-inflammatory transcriptional program in dendritic cells from RA patients and underscore the relevance of the TNF receptor profile in shaping DC function. These findings provide new insights into the immunobiology of RA and identify dendritic cells as potential targets for personalized immunomodulatory therapy.

The study of the structural and dynamic properties of multidomain proteins is of great interest as they constitute one of the main regulatory mechanisms in the ligand–receptor system. The interleukin-1 (IL-1) family is a group of proinflammatory, pleiotropic cytokines whose activity is primarily mediated through two primary signaling receptors—the IL-1 type I receptor (IL-1R1) and the IL-1 receptor accessory protein (IL-1R3), which acts as a coreceptor—and a decoy receptor, the IL-1 type II receptor (IL-1R2). Their key ligands include the agonists IL-1α and IL-1β, and the endogenous antagonist IL-1 receptor antagonist (IL-1Ra). Evaluating the interdomain movements of these receptors is expected to shed light on the mechanisms of ligand binding and the development of a functional cellular response. Furthermore, analysis of the literature allows us to identify key aspects of the conformational dynamics of IL-1 receptors. It is established that the flexibility of the domains of these receptors plays a crucial role in their ability to adapt to various ligands and ensure accurate signal transmission in the cell. Analysis of the structural and functional features of these receptors allows us to determine the mechanisms underlying their interactions and identify differences in their functional activities. Understanding the structural mechanisms that give rise to domain flexibility can enable us to develop molecules which are capable of specifically modulating the activity of these receptors. This facet is especially important in the context of treating inflammatory and autoimmune diseases for the creation of new approaches to therapy.

Introduction: Tumor necrosis factor (TNFα) is an important proinflammatory cytokine in rheumatoid arthritis (RA) immune processes. However, TNFα activity and functions may be regulated by soluble receptors, which act as decoys, and by number, density, and co-expression of its membrane-bound receptors type 1 and 2 (TNFR1 and TNFR2). The aim of this study was to reveal associations between TNFR1/2 co-expression profile parameters and RA disease activity indicators. Methods: PBMC were analyzed from 46 healthy donors and 64 patients with RA using flow cytometry. Patients were divided according to the disease activity score (DAS) 28 index into groups with high (n = 22, 34.4%), moderate (n = 30, 46.9%), and low (n = 12, 18.8%) disease activity. Co-expression of TNFR1 and TNFR2 was studied by evaluating the percentage of cells, with different receptors, and by counting the number of receptors of each type per cell, using QuantiBritePE beads. Associations between disease severity and activity indicators and parameters of TNFα receptor expression in subpopulations of immune cells were studied. Results: T cell subsets from RA patients were characterized by co-expression of TNFR1 and TNFR2, and were found to differ significantly compared with healthy donors. Memory cells both among T helper cells and cytotoxic T cells demonstrated the most significant differences in TNFR-expression profile. Multivariable logistic regression revealed model to identified RA patients from healthy individual based on the TNFR1/2 co-expression parameters. Conclusion: The profile of TNFR1\2 co-expression differs in RA comparing with health. Proportion of TNFR1+TNFR2- cells increased significantly among memory T helper cells and activated cytotoxic T cells, and decreased significantly among naïve cytotoxic T cells and T regulatory cells as compared with health. The parameters of TNFR1\2 co-expression in RA are associated with clinical and laboratory indicators of disease activity.

Background: The co-expression patterns of type 1 and 2 tumor necrosis factor (TNF)-α membrane receptors (TNFR1/TNFR2) are associated with the presence, stage, and activity of allergic diseases. The aim of this study was to assess the expression levels and dynamics of TNFRs on immune cells and to assess associations between their expression and severity of bronchial asthma (BA). Methods: Patients with severe (n = 8), moderate (n = 10), and mild (n = 4) BA were enrolled. As a comparison group, data from 46 healthy volunteers (HV) were accessed. Co-expression of TNFR1/2 was evaluated as a percentage of cells and the number of receptors of each type per cell. Multivariate logistic regression analysis was used to identify diagnostic biomarkers of BA. Results: More than 90% of the monocytes in patients with mild BA were TNFR1+TNFR2+ but had significantly lower TNFR1 expression density compared with HV (7.82- to 14.08-fold, depending on disease severity). Lower percentages of the TNFR+ B-lymphocytes were observed in combination with significantly lower receptors density in BA compared with HV (2.59- to 11.64-fold for TNFR1 and 1.72- to 3.4-fold for TNFR2, depending on disease severity). The final multivariate model for predicting the presence of BA included the percentage of double-positive CD5+ B-lymphocytes and average number of TNFR1 molecules expressed on cytotoxic naive T-lymphocytes and T-helper cells (R² = 0.87). Conclusions: The co-expression patterns of TNFRs on immune cells in BA differed significantly compared with HV. The expression differences were associated with disease severity. TNFR1 expression changes were key parameters that discriminated patients with BA from those with HV.

This research delves into the intricate landscape of tumor necrosis factor-alpha (TNF-α) signaling, a multi-functional cytokine known for its diverse cellular effects. Specifically, we investigate the roles of two TNF receptors, TNFR1 and TNFR2, in mediating TNF-α-induced transcriptional responses. Using human K562 cell lines with TNFR1 and TNFR2 knockouts, we explore changes in gene expression patterns following TNF-α stimulation. Our findings reveal distinct transcriptional profiles in TNFR1 and TNFR2 knockout cells, shedding light on the unique contributions of these receptors to TNF-α signaling. Notably, several key pathways associated with inflammation, apoptosis, and cell proliferation exhibit altered regulation in the absence of TNFR1 or TNFR2. This study provides valuable insights into the intricate mechanisms governing TNF-α signaling and its diverse cellular effects, with potential implications for targeted therapeutic strategies.

Introduction: Immunosenescence alters TNF receptor expression (TNFR1 and TNFR2), contributing to chronic inflammation (inflamm-aging) and age-related diseases. Polymorphisms in TNFRSF1A and TNFRSF1B may influence receptor expression; however, their role in age-dependent modulation remains unclear. This study examines TNFR1/TNFR2 expression dynamics on T cells, B cells, and monocytes across different ages and evaluates the impact of genetic polymorphisms. Methods: PBMCs from 150 donors (18–60 years) were isolated via density-gradient centrifugation and cultured under spontaneous and LPS-stimulated conditions. TNFR1 and TNFR2 expression on immune cell subsets was quantified using flow cytometry with BD QuantiBRITE PE beads. SNP genotyping in TNFRSF1A and TNFRSF1B was performed via PCR with restriction analysis. Nonlinear age-related trends were assessed using polynomial approximation and inflection point analysis (Tukey’s method). Results: Among the 23 analyzed TNF system parameters, the proportion of TNFR2⁺CD3⁺ T cells increased with age, whereas TNFR1⁺ and TNFR2⁺ monocyte populations showed significant negative correlations (p < 0.05). Inflection points (~27, 34–36, and 44–45 years) indicated nonlinear dynamics in TNFRs expression during aging. TNFR2 expression on T cells gradually increased and stabilized at later ages, whereas TNFR1 and TNFR2 expression on monocytes followed distinct declining trajectories. Genetic polymorphisms influenced correlation strength, but did not alter direction, demonstrating a conserved pattern of age-related receptor expression shifts. Conclusions: TNFR expression exhibits nonlinear, age-dependent alterations across immune cells, shaped by immunosenescence and genetic variability. The identified critical age intervals represent key phases of immune remodeling, where assessing TNFR expression may provide insights into inflamm-aging mechanisms and potential targets for immune modulation.