Objectives To predict response prior to anti‐TNF treatment and comprehensively understand the mechanism how patients respond differently to anti‐TNF treatment in rheumatoid arthritis (RA). Methods Gene expression and/or DNA methylation profiling on PBMC, monocytes, and CD4⁺ T cells, from 80 RA patients before initiating either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6‐month treatment response was evaluated according to the EULAR criteria of disease response. Differential expression and methylation analyses were performed to identify the response‐associated transcriptional and epigenetic signatures. Machine learning models were built using these signatures by random forest algorithm to predict response prior to anti‐TNF treatment and were further validated by a follow‐up study. Results Transcriptional signatures in ADA and ETN responders were divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4⁺ T cells. The genes upregulated in CD4⁺ T cells of ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differentially methylated positions (DMPs) of responders to ETN but not to ADA were majorly hypermethylated. The machine learning models to predict the response to ADA and ETN using differential genes reached overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow‐up study validated the high performance of these models. Conclusions Machine learning models based on these molecular signatures could accurately predict response before ADA and ETN treatment, paving the path towards personalized anti‐TNF treatment.

Chronic inflammatory diseases are characterized by recurrent inflammatory attacks in the tissues mediated by autoreactive T cells. Identity and functional programming of CD8⁺ T cells at the target site of inflammation still remain elusive. One key question is whether, in these antigen-rich environments, chronic stimulation leads to CD8⁺ T cell exhaustion comparable to what is observed in infectious disease contexts. In the synovial fluid (SF) of juvenile idiopathic arthritis (JIA) patients, a model of chronic inflammation, an overrepresentation of PD-1⁺CD8⁺ T cells was found. Gene expression profiling, gene set enrichment analysis, functional studies, and extracellular flux analysis identified PD-1⁺CD8⁺ T cells as metabolically active effectors, with no sign of exhaustion. Furthermore, PD-1⁺CD8⁺ T cells were enriched for a tissue-resident memory (Trm) cell transcriptional profile and demonstrated increased clonal expansion compared with the PD-1^– counterpart, suggesting antigen-driven expansion of locally adapted cells. Interestingly, this subset was also found increased in target tissues in other human chronic inflammatory diseases. These data indicate that local chronic inflammation drives the induction and expansion of CD8⁺ T cells endowed with potential detrimental properties. Together, these findings lay the basis for investigation of PD-1–expressing CD8⁺ T cell targeting strategies in human chronic inflammatory diseases.

Treg cells are critical regulators of immune homeostasis, and environment-driven Treg cell differentiation into effector (e)Treg cells is crucial for optimal functioning. However, human Treg cell programming in inflammation is unclear. Here, we combine transcriptional and epigenetic profiling to identify a human eTreg cell signature. Inflammation-derived functional Treg cells have a transcriptional profile characterized by upregulation of both a core Treg cell (FOXP3, CTLA4, TIGIT) and effector program (GITR, BLIMP-1, BATF). We identify a specific human eTreg cell signature that includes the vitamin D receptor (VDR) as a predicted regulator in eTreg cell differentiation. H3K27ac/H3K4me1 occupancy indicates an altered (super-)enhancer landscape, including enrichment of the VDR and BATF binding motifs. The Treg cell profile has striking overlap with tumor-infiltrating Treg cells. Our data demonstrate that human inflammation-derived Treg cells acquire a conserved and specific eTreg cell profile guided by epigenetic changes, and fine-tuned by environment-specific adaptations.

Objectives Studies that compare immune cell phenotype and function in psoriatic arthritis (PsA) versus psoriasis limited to cutaneous involvement (Pso) are scarce, yet critical to understand the pathogenesis of PsA. Methods First, in‐depth immunophenotyping of different T cell and Dendritic cell subsets was performed in PsA, Pso, axial spondyloarthritis patients and healthy controls. Subsequently, we analyzed cells from peripheral blood, synovial fluid, and skin biopsies using flow cytometry, alongside high‐throughput transcriptome analyses and functional assays on the specific cell population differentiating PsA from Pso. Results As compared to HC, the peripheral blood of PsA was characterized by an increase of regulatory CD4 T cells and IL‐17A & IL‐22 co‐producing CD8+ T cells. One population specifically differentiated PsA from Pso: CCR10+ CD8+ T cells, being enriched in PsA. CCR10+ CD8+ T cells were phenotypically DNAM1^hi, effector memory cells that co‐expressed skin homing receptors CCR4 and CLA. CCR10+ CD8+ T cells were detected under inflammatory and homeostatic conditions in skin, but were not enriched in synovial fluid. Gene profiling further revealed that CCR10+ CD8+ T cells expressed GATA3, FOXP3, and core transcriptional signature of tissue‐resident memory T cells, including CD103. Specific genes (including RORC, IFNAR1, ERAP1) were upregulated in PsA compared to Pso. CCR10+ CD8+ were endowed with Tc2/22‐like cytokine profile, lacked cytotoxic potential, and displayed overall regulatory function. Conclusion Tissue‐resident memory CD8+ T cells derived from the skin are enhanced in the circulation of PsA as compared to Pso. This may indicate aberrances in cutaneous tissue homeostasis contribute to arthritis development.

Objective The interferon (IFN) signature is related to disease activity and vascular disease in systemic lupus erythematosus (SLE) and antiphospholipid syndrome (APS) and represents a promising therapeutic target. Quantification of the IFN signature is currently performed by gene expression analysis, limiting its current applicability in clinical practice. Therefore, the objective of this study was to establish an easy to measure biomarker for the IFN signature. Methods Serum levels of galectin-9, CXCL-10 (IP-10) and tumour necrosis factor receptor type II (TNF-RII) were measured in patients with SLE, SLE+APS and primary APS (PAPS) and healthy controls (n=148) after an initial screening of serum analytes in a smaller cohort (n=43). Analytes were correlated to measures of disease activity and the IFN signature. The performance of galectin-9, CXCL-10 and TNF-RII as biomarkers to detect the IFN signature was assessed by receiver operating characteristic curves. Results Galectin-9, CXCL-10 and TNF-RII were elevated in patients with SLE, SLE+APS and PAPS (p<0.05) and correlated with disease activity and tissue factor expression. Galectin-9 correlated stronger than CXCL-10 or TNF-RII with the IFN score (r=0.70, p<0.001) and was superior to CXCL-10 or TNF-RII in detecting the IFN signature (area under the curve (AUC) 0.86). Importantly, in patients with SLE(±APS), galectin-9 was also superior to anti-dsDNA antibody (AUC 0.70), or complement C3 (AUC 0.70) and C4 (AUC 0.78) levels in detecting the IFN signature. Conclusion Galectin-9 is a novel, easy to measure hence clinically applicable biomarker to detect the IFN signature in patients with systemic autoimmune diseases such as SLE and APS.