In silico degradomics reveals disease- and endotype-specific alterations in the joint tissue landscape
Tissues dynamically remodel extracellular matrix to maintain homeostasis, alterations in which are an early pathogenic hallmark of disease. Protein degradation, essential for tissue remodelling, is often dismissed as indiscriminate damage, despite evidence of its specificity. A major determinant of protein tissue levels and activity, matrix proteolysis also creates circulating degradation products that are emerging biomarkers, with specific collagen fragments capable of tracking disease severity. Understanding intentional matrix destruction therefore is key to understanding tissue biology. Unbiased, holistic analysis, extending our knowledge beyond ubiquitously expressed collagens, will uncover tissue- and disease-specific remodelling. However, degradomics technical demands, requiring labelling and enrichment for neo-epitopes generated by cleavage events, restricts its inclusion in omics research. Here, we develop an in-silico pipeline (DegrAID) that identifies semi-tryptic peptides in unlabelled/unenriched proteomic datasets, mapping neo-epitopes within matrix domain organization and 3D structure, correlating these with known/predicted protease sites, and applies this to rare patient cohorts. Validation with matched degradomic data showed good conservation across degraded proteins and cleavage sites. Interrogation of multiple, independent cohorts including cartilage, synovial tissue and synovial fluid from osteoarthritis (OA) or rheumatoid arthritis (RA) patients identified distinct degradomes between disease and tissue compartments. Further investigating RA heterogeneity revealed myeloid and lymphoid endotypes that display different treatment responses, have substantially different degradation patterns. Proteoglycans were more degraded in myeloid-RA, while collagens more so in lymphoid-RA, with notable exceptions, and endotype-specific fingerprints were conserved between synovial tissue and fluid. Thus, this tool provides new insights into tissue remodelling by unlocking degradomes from any proteomic dataset. One Sentence SummaryDisease- and endotype- specific degradomes generated from clinical proteomics datasets, reveal distinct tissue remodeling patterns.