Proteases control complex tissue responses by modulating inflammation, cell proliferation and migration, and matrix remodeling. All these processes are orchestrated in cutaneous wound healing to restore the skin9s barrier function upon injury. Altered protease activity has been implicated in the pathogenesis of healing impairments, and proteases are important targets in diagnosis and therapy of this pathology. Global assessment of proteolysis at critical turning points after injury will define crucial events in acute healing that might be disturbed in healing disorders. As optimal biospecimens, wound exudates contain an ideal proteome to detect extracellular proteolytic events, are noninvasively accessible, and can be collected at multiple time points along the healing process from the same wound in the clinics. In this study, we applied multiplexed Terminal Amine Isotopic Labeling of Substrates (TAILS) to globally assess proteolysis in early phases of cutaneous wound healing. By quantitative analysis of proteins and protein N termini in wound fluids from a clinically relevant pig wound model, we identified more than 650 proteins and discerned major healing phases through distinctive abundance clustering of markers of inflammation, granulation tissue formation, and re-epithelialization. TAILS revealed a high degree of proteolysis at all time points after injury by detecting almost 1300 N-terminal peptides in ∼450 proteins. Quantitative positional proteomics mapped pivotal interdependent processing events in the blood coagulation and complement cascades, temporally discerned clotting and fibrinolysis during the healing process, and detected processing of complement C3 at distinct time points after wounding and by different proteases. Exploiting data on primary cleavage specificities, we related candidate proteases to cleavage events and revealed processing of the integrin adapter protein kindlin-3 by caspase-3, generating new hypotheses for protease-substrate relations in the healing skin wound in vivo. The data have been deposited to the ProteomeXchange Consortium with identifier PXD001198.

Proteolysis is an irreversible post-translational modification that affects intra- and intercellular communication by modulating the activity of bioactive mediators. Key to understanding protease function is the system-wide identification of cleavage events and their dynamics in physiological contexts. Despite recent advances in mass spectrometry-based proteomics for high-throughput substrate screening, current approaches suffer from high false positive rates and only capture single states of protease activity. Here, we present a workflow based on multiplexed terminal amine isotopic labeling of substrates for time-resolved substrate degradomics in complex proteomes. This approach significantly enhances confidence in substrate identification and categorizes cleavage events by specificity and structural accessibility of the cleavage site. We demonstrate concomitant quantification of cleavage site spanning peptides and neo-N and/or neo-C termini to estimate relative ratios of noncleaved and cleaved forms of substrate proteins. By applying this strategy to dissect the matrix metalloproteinase 10 (MMP10) substrate degradome in fibroblast secretomes, we identified the extracellular matrix protein ADAMTS-like protein 1 (ADAMTSL1) as a direct MMP10 substrate and revealed MMP10-dependent ectodomain shedding of platelet-derived growth factor receptor alpha (PDGFRα) as well as sequential processing of type I collagen. The data have been deposited to the ProteomeXchange Consortium with identifier PXD000503.

The pre-metastatic niche (PMN) is a tumor-driven microenvironment in distant organs that can foster and support the survival and growth of disseminated tumor cells. This facilitates the establishment of secondary lesions that eventually form overt metastasis, the main cause of cancer-related death. In recent years, tumor-derived extracellular-vesicles (EVs) have emerged as potentially key drivers of the PMN. The role of the PMN in osteosarcoma metastasis is poorly understood and the potential contribution of osteosarcoma cell-derived EVs to PMN formation has not been investigated so far. Here, we characterize pulmonary PMN development using the spontaneously metastasizing 143-B xenograft osteosarcoma mouse model. We demonstrate the accumulation of CD11b⁺ myeloid cells in the pre-metastatic lungs of tumor-bearing mice. We also establish that highly metastatic 143-B and poorly metastatic SAOS-2 osteosarcoma cell-derived EV education in naïve mice can recapitulate the recruitment of myeloid cells to the lungs. Surprisingly, despite EV-induced myeloid cell infiltration in the pre-metastatic lungs, 143-B and SAOS-2 EVs do not contribute towards the 143-B metastatic burden in the context of both spontaneous as well as experimental metastasis in severe-combined immunodeficient (SCID) mice. Taken together, OS-derived EVs alone may not be able to form a functional PMN, and may perhaps require a combination of tumor-secreted factors along with EVs to do so. Additionally, our study gives a valuable insight into the PMN complexity by providing the transcriptomic signature of the premetastatic lungs in an osteosarcoma xenograft model for the first time. In conclusion, identification of regulators of cellular and molecular changes in the pre-metastatic lungs might lead to the development of a combination therapies in the future that interrupt PMN formation and combat osteosarcoma metastasis.

Epilepsy is a common neurological disorder characterized by recurrent epileptic seizures. These seizures have different intensities and might lead to accidents or, in the worst case, to sudden death. Therefore, being able to predict epileptic seizures would allow patients to be prepared, reducing the risk of injury. This paper focuses on epileptic seizure prediction using EEG (Electroencephalogram) signals. In contrast to the standard approach where the preictal state is assumed to have a constant duration in all the seizures of a patient, we propose a new method that labels each seizure individually exploiting clustering. Our labeling approach, which was applicable for 38% of the selected seizures, results in substantial improvements compared to the standard one. In fact, it reduces noise in the labels and improves the performance of the binary classifier used to distinguish the interictal and preictal states. Hence, our results suggest that the preictal duration is seizure-specific, not only patient-specific. Finally, we show that our method is able to predict 17 out of 18 (94%) seizures between 15 and 85 minutes, before seizure onset.