bioRxivImmunology
Chemokines form complex signals during inflammation and disease that can be decoded by extracellular matrix proteoglycans
A. J. Ridley, Y. Ou, R. Karlsson, N. Pun, H. L. Birchenough, T. A. Jowitt, C. Lawless, R. L. Miller, D. P. Dyer
Abstract
Chemokine driven leukocyte recruitment is a key component of the immune response and is central to a wide range of diseases. However, there has yet to be a clinically successful therapeutic approach that targets the chemokine system during inflammatory disease; possibly due to the supposed redundancy of the chemokine system. A range of recent studies have demonstrated that the chemokine system is in fact based on specificity of function. Here we have generated a resource to analyse chemokine gene (ligand and receptor) expression across different species, tissues and diseases; revealing complex expression patterns whereby multiple chemokine ligands that mediate recruitment of the same leukocyte type are expressed in the same context, e.g. the CXCR3 ligands CXCL9, 10 and 11. We use biophysical approaches to show that CXCL9, 10 and 11 have very different interactions with extracellular matrix glycosaminoglycans (GAGs) which is exacerbated by specific GAG sulphation. Finally, in vivo approaches demonstrate that GAG-binding is critical for CXCL9 driven recruitment of specific T cell subsets (e.g. CD4+) but not others (e.g. CD8+), independent of CXCR3 expression. Our data demonstrate that chemokine expression is complex and that multiple ligands are likely needed for robust leukocyte recruitment across tissues and diseases. We also demonstrate that ECM GAGs facilitate decoding of these complex chemokine signals so that they are either primarily presented on GAG-coated cell surfaces or remain more soluble. Our findings represent a new mechanistic understanding of chemokine mediated immune cell recruitment and identify novel avenues to target specific chemokines during inflammatory disease.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: cell-cell communication
- approach: biophysical modeling
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