Chemokines form complex signals during inflammation and disease that can be decoded by extracellular matrix proteoglycans.
Amanda J L RidleyYaqing OuRichard Torbjörn Gustav KarlssonNabina PunHolly L BirchenoughIashia Z MulhollandMary L BirchAndrew S MacDonaldThomas A JowittCraig LawlessRebecca Louise MillerDouglas P DyerPublished in: Science signaling (2023)
Chemokine-driven leukocyte recruitment is a key component of the immune response and of various diseases. Therapeutically targeting the chemokine system in inflammatory disease has been unsuccessful, which has been attributed to redundancy. We investigated why chemokines instead have specific, specialized functions, as demonstrated by multiple studies. We analyzed the expression of genes encoding chemokines and their receptors across species, tissues, and diseases. This analysis revealed complex expression patterns such that genes encoding multiple chemokines that mediated recruitment of the same leukocyte type were expressed in the same context, such as the genes encoding the CXCR3 ligands CXCL9, CXCL10, and CXCL11. Through biophysical approaches, we showed that these chemokines differentially interacted with extracellular matrix glycosaminoglycans (ECM GAGs), which was enhanced by sulfation of specific GAGs. Last, in vivo approaches demonstrated that GAG binding was critical for the CXCL9-dependent recruitment of specific T cell subsets but not of others, irrespective of CXCR3 expression. Our data demonstrate that interactions with ECM GAGs regulated whether chemokines were presented on cell surfaces or remained more soluble, thereby affecting chemokine availability and ensuring specificity of chemokine action. Our findings provide a mechanistic understanding of chemokine-mediated immune cell recruitment and identify strategies to target specific chemokines during inflammatory disease.
Keyphrases
- extracellular matrix
- poor prognosis
- immune response
- genome wide
- oxidative stress
- binding protein
- single cell
- peripheral blood
- gene expression
- dna methylation
- bioinformatics analysis
- genome wide identification
- stem cells
- transcription factor
- mass spectrometry
- drug delivery
- electronic health record
- cell therapy
- toll like receptor
- pseudomonas aeruginosa
- big data
- high resolution
- cancer therapy
- artificial intelligence
- dendritic cells
- inflammatory response
- machine learning
- genome wide analysis
- deep learning