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Monocytes/macrophages express CCR9 in rheumatoid arthritis and CCL25 stimulates their differentiationAbstract Introduction Monocytes/macrophages accumulate in the rheumatoid (RA) synovium where they play a central role in inflammation and joint destruction. Identification of molecules involved in their accumulation and differentiation is important to inform therapeutic strategies. This study investigated the expression and function of chemokine receptor CCR9 in the peripheral blood (PB) and synovium of RA, non-RA patients and healthy volunteers. Methods CCR9 expression on PB monocytes/macrophages was analysed by flow cytometry and in synovium by immunofluorescence. Chemokine receptor CCR9 mRNA expression was examined in RA and non-RA synovium, monocytes/macrophages from PB and synovial fluid (SF) of RA patients and PB of healthy donors using the reverse transcription polymerase chain reaction (RT-PCR). Monocyte differentiation and chemotaxis to chemokine ligand 25 (CCL25)/TECK were used to study CCR9 function. Results CCR9 was expressed by PB monocytes/macrophages in RA and healthy donors, and increased in RA. In RA and non-RA synovia, CCR9 co-localised with cluster of differentiation 14+ (CD14+) and cluster of differentiation 68+ (CD68+) macrophages, and was more abundant in RA synovium. CCR9 mRNA was detected in the synovia of all RA patients and in some non-RA controls, and monocytes/macrophages from PB and SF of RA and healthy controls. CCL25 was detected in RA and non-RA synovia where it co-localised with CD14+ and CD68+ cells. Tumour necrosis factor alpha (TNFα) increased CCR9 expression on human acute monocytic leukemia cell line THP-1 monocytic cells. CCL25 induced a stronger monocyte differentiation in RA compared to healthy donors. CCL25 induced significant chemotaxis of PB monocytes but not consistently among individuals. Conclusions CCR9 expression by monocytes is increased in RA. CCL25 may be involved in the differentiation of monocytes to macrophages particularly in RA.
Two independent proteomic approaches provide a comprehensive analysis of the synovial fluid proteome response to Autologous Chondrocyte ImplantationBackground: Autologous chondrocyte implantation (ACI) has a failure rate of approximately 20%, but it is yet to be fully understood why. Biomarkers are needed that can pre-operatively predict in which patients it is likely to fail, so that alternative or individualised therapies can be offered. We previously used label-free quantitation (LF) with a dynamic range compression proteomic approach to assess the synovial fluid (SF) of ACI responders and non-responders. However, we were able to identify only a few differentially abundant proteins at baseline. In the present study, we built upon these previous findings by assessing higher-abundance proteins within this SF, providing a more global proteomic analysis on the basis of which more of the biology underlying ACI success or failure can be understood. Methods: Isobaric tagging for relative and absolute quantitation (iTRAQ) proteomic analysis was used to assess SF from ACI responders (mean Lysholm improvement of 33; n = 14) and non-responders (mean Lysholm decrease of 14; n = 13) at the two stages of surgery (cartilage harvest and chondrocyte implantation). Differentially abundant proteins in iTRAQ and combined iTRAQ and LF datasets were investigated using pathway and network analyses. Results: iTRAQ proteomic analysis confirmed our previous finding that there is a marked proteomic shift in response to cartilage harvest (70 and 54 proteins demonstrating ≥ 2.0-fold change and p < 0.05 between stages I and II in responders and non-responders, respectively). Further, it highlighted 28 proteins that were differentially abundant between responders and non-responders to ACI, which were not found in the LF study, 16 of which were altered at baseline. The differential expression of two proteins (complement C1s subcomponent and matrix metalloproteinase 3) was confirmed biochemically. Combination of the iTRAQ and LF proteomic datasets generated in-depth SF proteome information that was used to generate interactome networks representing ACI success or failure. Functional pathways that are dysregulated in ACI non-responders were identified, including acute-phase response signalling. Conclusions: Several candidate biomarkers for baseline prediction of ACI outcome were identified. A holistic overview of the SF proteome in responders and non-responders to ACI has been profiled, providing a better understanding of the biological pathways underlying clinical outcome, particularly the differential response to cartilage harvest in non-responders.