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Background: In rheumatoid arthritis (RA) macrophages play a major role in amplifying synovial inflammation. Important activating signals are those induced by Toll-like receptor (TLR) ligands and by activated T cells. The balance between activating and inhibitory Fc gamma receptors (FcγRs) on macrophages might be crucial in modulating these inflammatory responses. The purpose of this study was to determine FcγR expression on pro- and anti-inflammatory macrophages (gmMφ and mMφ, respectively) and identify functional consequences on immune complex uptake and macrophage activation. Methods: Human monocytes were isolated and differentiated into gmMφ and mMφ. A full FcγR characterization of both macrophage subtypes was performed and uptake of fluorescent immune complexes (ICs) was determined. FcγRIIb isoforms were determined by qPCR. Macrophages were stimulated via different TLRs or cytokine activated T cells in the presence or absence of ICs and cytokine production was determined. Blocking studies were performed to look into the pathways involved. Results: mMφ expressed high levels of the activating FcγRIIa and FcγRIII and low levels of the inhibitory FcγRIIb, while the FcγR balance on gmMφ was shifted towards the inhibitory FcγRIIb. This was accompanied by a clear increase in FcγRIIb1 mRNA expression in gmMφ. This resulted in higher IC uptake by mMφ compared to gmMφ. Furthermore, FcγR-mediated stimulation of gmMφ inhibited TLR2, 3, 4 and 7/8 mediated cytokine production via FcγRIIb and PI3K signaling. In addition, gmMφ but not mMφ produced TNFα upon co-culture with cytokine activated T cells, which was reduced by IC binding to FcγRIIb. The latter was dependent on PI3K signaling and COX2. Conclusions: FcγR expression patterns on gmMφ and mMφ are significantly different, which translates in clear functional differences further substantiating FcγRIIb as an interesting target for inflammation control in RA and other autoimmune/inflammatory diseases. [ABSTRACT FROM AUTHOR]

Psoriasis (Pso) is a chronic inflammatory skin disease, and up to 30% of Pso patients develop psoriatic arthritis (PsA), which can lead to irreversible joint damage. Early detection of PsA in Pso patients is crucial for timely treatment but difficult for dermatologists to implement. We, therefore, aimed to find disease-specific immune profiles, discriminating Pso from PsA patients, possibly facilitating the correct identification of Pso patients in need of referral to a rheumatology clinic. The phenotypes of peripheral blood immune cells of consecutive Pso and PsA patients were analyzed, and disease-specific immune profiles were identified via a machine learning approach. This approach resulted in a random forest classification model capable of distinguishing PsA from Pso (mean AUC = 0.95). Key PsA-classifying cell subsets selected included increased proportions of differentiated CD4+CD196+CD183-CD194+ and CD4+CD196-CD183-CD194+ T-cells and reduced proportions of CD196+ and CD197+ monocytes, memory CD4+ and CD8+ T-cell subsets and CD4+ regulatory T-cells. Within PsA, joint scores showed an association with memory CD8+CD45RA-CD197- effector T-cells and CD197+ monocytes. To conclude, through the integration of in-depth flow cytometry and machine learning, we identified an immune cell profile discriminating PsA from Pso. This immune profile may aid in timely diagnosing PsA in Pso. [ABSTRACT FROM AUTHOR]