bioRxivImmunology
Loss of α2-6 sialylation promotes the transformation of synovial fibroblasts into a pro-inflammatory phenotype in Rheumatoid Arthritis.
Y. Wang, A. Khan, A. Antonopoulos, L. Bouche, C. D. Buckley, A. Filer, K. Raza, K. Ping-Li, B. Tolusso, E. Gremese, M. Kurowska-Stolarska, S. Alivernini, A. Dell, S. M. Haslam, M. A. Pineda
Abstract
In healthy joints, synovial fibroblasts (SFs) provide the microenvironment required to mediate homeostasis but are recognized to adopt a pathological role in rheumatoid arthritis (RA), promoting the infiltration and activation of immune cells to perpetuate local inflammation, pain and joint destruction. Carbohydrates (glycans) attached to cell surface proteins are fundamental regulators of cellular interactions between stromal and immune cells, but very little is known about the glycome of SFs or how glycosylation regulates their biology. Here we fill these gaps in our understanding of stromal guided pathophysiology by systematically mapping glycosylation pathways in healthy and arthritic SFs. We used a combination of transcriptomic and glycomic analysis to show that transformation of fibroblasts into pro-inflammatory cells in RA is associated with profound glycan remodeling, a process that involves reduction of 2-6 terminal sialylation that is mostly mediated by TNF-dependent inhibition of the glycosyltransferase ST6Gal1. We also show that sialylation of SFs correlates with distinct disease stages and SFs functional subsets in both human RA and models of mouse arthritis. We propose that pro-inflammatory cytokines in the joint remodel the SF-glycome, transforming a regulatory tissue intended to preserve local homeostasis, into an under-sialylated and highly pro-inflammatory microenvironment that contributes to an amplificatory inflammatory network that perpetuates chronic inflammation. These results highlight the importance of cell glycosylation in stromal immunology.
- organism: human
- organism: mouse
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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