Matrix metalloproteinases (MMPs) and A Disintegrin and Metalloproteinases (ADAMs) are two related protease families that play key roles in matrix remodeling and growth factor ligand shedding. Directly ascertaining the proteolytic activities of particular MMPs and ADAMs in physiological environments in a non-invasive, real-time, multiplex manner remains a challenge. This work describes Proteolytic Activity Matrix Analysis (PrAMA), an integrated experimental measurement and mathematical analysis framework for simultaneously determining the activities of particular enzymes in complex mixtures of MMPs and ADAMs. The PrAMA method interprets dynamic signals from panels of moderately specific FRET-based polypeptide protease substrates to deduce a profile of specific MMP and ADAM proteolytic activities. Deconvolution of signals from complex mixtures of proteases is accomplished using prior data on individual MMP/ADAM cleavage signatures for the substrate panel measured with purified enzymes. We first validate PrAMA inference using a compendium of roughly 4000 measurements involving known mixtures of purified enzymes and substrates, and then demonstrate application to the live-cell response of wildtype, ADAM10−/−, and ADAM17−/− fibroblasts to phorbol ester stimulation. Results indicate PrAMA can distinguish closely related enzymes from each other with high accuracy, even in the presence of unknown background proteolytic activity. PrAMA offers a valuable tool for applications ranging from live-cell in vitro assays to high-throughput inhibitor screening with complex enzyme mixtures. Moreover, our approach may extend to other families of proteases, such as caspases and cathepsins, that also can lack highly-specific substrates.

A Disintegrin and Metalloproteinases (ADAMs) are the principal enzymes for shedding receptor tyrosine kinase (RTK) ectodomains and ligands from the cell surface. Multiple layers of activity regulation, feedback, and catalytic promiscuity impede our understanding of context-dependent ADAM "sheddase" function and our ability to predictably target that function in disease. This study uses combined measurement and computational modeling to examine how various growth factor environments influence sheddase activity and cell migration in the invasive disease of endometriosis. We find that ADAM-10 and -17 dynamically integrate numerous signaling pathways to direct cell motility. Data-driven modeling reveals that induced cell migration is a quantitative function of positive feedback through EGF ligand release and negative feedback through RTK shedding. Although sheddase inhibition prevents autocrine ligand shedding and resultant EGF receptor transactivation, it also leads to an accumulation of phosphorylated receptors (HER2, HER4, and MET) on the cell surface, which subsequently enhances Jnk/p38 signaling. Jnk/p38 inhibition reduces cell migration by blocking sheddase activity while additionally preventing the compensatory signaling from accumulated RTKs. In contrast, Mek inhibition reduces ADAM-10 and -17 activities but fails to inhibit compensatory signaling from accumulated RTKs, which actually enhances cell motility in some contexts. Thus, here we present a sheddase-based mechanism of rapidly acquired resistance to Mek inhibition through reduced RTK shedding that can be overcome with rationally directed combination inhibitor treatment. We investigate the clinical relevance of these findings using targeted proteomics of peritoneal fluid from endometriosis patients and find growth-factor-driven ADAM-10 activity and MET shedding are jointly dysregulated with disease.cell signaling networks | metalloproteinase activity | cue-signal-response analysis | amphiregulin