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.

Objective To predict response to anti–tumor necrosis factor (anti‐TNF) prior to treatment in patients with rheumatoid arthritis (RA), and to comprehensively understand the mechanism of how different RA patients respond differently to anti‐TNF treatment. Methods Gene expression and/or DNA methylation profiling on peripheral blood mononuclear cells (PBMCs), monocytes, and CD4+ T cells obtained from 80 RA patients before they began either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6 months, treatment response was evaluated according to the European League Against Rheumatism criteria for disease response. Differential expression and methylation analyses were performed to identify the response‐associated transcription and epigenetic signatures. Using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti‐TNF treatment, and were further validated by a follow‐up study. Results Transcription signatures in ADA and ETN responders were divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes up‐regulated in CD4+ T cells from ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differentially methylated positions (DMPs) were strongly hypermethylated in responders to ETN but not to ADA. The machine learning models for the prediction of response to ADA and ETN using differential genes reached an overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached an overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow‐up study validated the high performance of these models. Conclusion Our findings indicate that machine learning models based on molecular signatures accurately predict response before ADA and ETN treatment, paving the path toward personalized anti‐TNF treatment.