The endocytic receptor uPARAP is a regulator of extracellular thrombospondin-1.

Thrombospondin-1 (TSP-1) is a matricellular protein with a multitude of functions in the pericellular and extracellular environment. We report a novel pathway for the regulation of extracellular TSP-1, governed by the endocytic collagen receptor, uPARAP (urokinase plasminogen activator receptor-associated protein; MRC2 gene product, also designated Endo180, CD280). First, using a novel proteomic approach for unbiased identification of ligands for endocytosis, we identify TSP-1 as a candidate ligand for specific uptake by uPARAP. We then show that uPARAP can efficiently internalize TSP-1 for lysosomal degradation, that this capability is not shared by other, closely related endocytic receptors and that uPARAP serves to regulate the extracellular levels of TSP-1 in vitro. Using wild type and uPARAP null mice, we also demonstrate uPARAP-mediated endocytosis of TSP-1 in dermal fibroblasts in vivo. Unlike other uPARAP ligands, the interaction with TSP-1 is sensitive to heparin and the responsible molecular motifs in uPARAP are overlapping, but not identical with those governing the interaction with collagens. Finally, we show that uPARAP can also mediate the endocytosis of TSP-2, a thrombospondin closely related to TSP-1, but not the more distantly related members of the same protein family, TSP-3, -4 and -5. These findings indicate that the role of uPARAP in ECM remodeling is not limited to the uptake of collagen for degradation but also includes an orchestrator function in the regulation of thrombospondins with numerous downstream effects. This is likely to be an important factor in the physiological and pathological roles of uPARAP in bone biology, fibrosis and cancer. The proteomic data has been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD031272.

A Tutorial for Variance-Sensitive Clustering and the Quantitative Analysis of Protein Complexes.

Data clustering facilitates the identification of biologically relevant molecular features in quantitative proteomics experiments with thousands of measurements over multiple conditions. It finds groups of proteins or peptides with similar quantitative behavior across multiple experimental conditions. This co-regulatory behavior suggests that the proteins of such a group share their functional behavior and thus often can be mapped to the same biological processes and molecular subnetworks.While usual clustering approaches dismiss the variance of the measured proteins, VSClust combines statistical testing with pattern recognition into a common algorithm. Here, we show how to use the VSClust web service on a large proteomics data set and present further tools to assess the quantitative behavior of protein complexes.