Activatable Zymography Probes Enable In Situ Localization of Protease Dysregulation in Cancer

Imaging of proteases using activity-based probes

Proteases (proteolytic enzymes) are proteins that catalyze one of the most important biochemical reactions, namely the hydrolysis of the peptide bond in peptide and protein substrates. Therefore these molecular biocatalysts participate in virtually all living processes. The proper balance between intact and processed protease substrates enables to maintenance of homeostasis from a single-cell level to the whole living system. However, when the proteolytic activity is altered, this delicate balance is disturbed, which might lead to the development of a plethora of diseases. Given this, monitoring proteolytic activity is indispensable to understanding how proteases operate in disease lesions and how their altered catalytic activity might be harnessed for a better diagnosis and treatment. In this manuscript, we provide a critical review of the recent development of protease chemical probes which are small molecules that detect proteolytic activity by interacting with protease active site, individual proteases as well as complex proteolytic networks.

Low protease activity in B cell follicles promotes retention of intact antigens after immunization

The structural integrity of vaccine antigens is critical to the generation of protective antibody responses, but the impact of protease activity on vaccination in vivo is poorly understood. We characterized protease activity in lymph nodes and found that antigens were rapidly degraded in the subcapsular sinus, paracortex, and interfollicular regions, whereas low protease activity and antigen degradation rates were detected in the vicinity of follicular dendritic cells (FDCs). Correlated with these findings, immunization regimens designed to target antigen to FDCs led to germinal centers dominantly targeting intact antigen, whereas traditional immunizations led to much weaker responses that equally targeted the intact immunogen and antigen breakdown products. Thus, spatially compartmentalized antigen proteolysis affects humoral immunity and can be exploited.

Multiscale profiling of protease activity in cancer

Diverse processes in cancer are mediated by enzymes, which most proximally exert their function through their activity. High-fidelity methods to profile enzyme activity are therefore critical to understanding and targeting the pathological roles of enzymes in cancer. Here, we present an integrated set of methods for measuring specific protease activities across scales, and deploy these methods to study treatment response in an autochthonous model of Alk-mutant lung cancer. We leverage multiplexed nanosensors and machine learning to analyze in vivo protease activity dynamics in lung cancer, identifying significant dysregulation that includes enhanced cleavage of a peptide, S1, which rapidly returns to healthy levels with targeted therapy. Through direct on-tissue localization of protease activity, we pinpoint S1 cleavage to the tumor vasculature. To link protease activity to cellular function, we design a high-throughput method to isolate and characterize proteolytically active cells, uncovering a pro-angiogenic phenotype in S1-cleaving cells. These methods provide a framework for functional, multiscale characterization of protease dysregulation in cancer.

Protease Activity Analysis: A Toolkit for Analyzing Enzyme Activity Data

Analyzing the activity of proteases and their substrates is critical to defining the biological functions of these enzymes and to designing new diagnostics and therapeutics that target protease dysregulation in disease. While a wide range of databases and algorithms have been created to better predict protease cleavage sites, there is a dearth of computational tools to automate analysis of in vitro and in vivo protease assays. This necessitates individual researchers to develop their own analytical pipelines, resulting in a lack of standardization across the field. To facilitate protease research, here we present Protease Activity Analysis (PAA), a toolkit for the preprocessing, visualization, machine learning analysis, and querying of protease activity data sets. PAA leverages a Python-based object-oriented implementation that provides a modular framework for streamlined analysis across three major components. First, PAA provides a facile framework to query data sets of synthetic peptide substrates and their cleavage susceptibilities across a diverse set of proteases. To complement the database functionality, PAA also includes tools for the automated analysis and visualization of user-input enzyme-substrate activity measurements generated through in vitro screens against synthetic peptide substrates. Finally, PAA supports a set of modular machine learning functions to analyze in vivo protease activity signatures that are generated by activity-based sensors. Overall, PAA offers the protease community a breadth of computational tools to streamline research, taking a step toward standardizing data analysis across the field and in chemical biology and biochemistry at large.

Selective targeting of metastatic ovarian cancer using an engineered anthrax prodrug activated by membrane-anchored serine proteases

Treatments for advanced and recurrent ovarian cancer remain a challenge due to a lack of potent, selective, and effective therapeutics. Here, we developed the basis for a transformative anticancer strategy based on anthrax toxin that has been engineered to be selectively activated by the catalytic power of zymogen-activating proteases on the surface of malignant tumor cells to induce cell death. Exposure to the engineered toxin is cytotoxic to ovarian tumor cell lines and ovarian tumor spheroids derived from patient ascites. Preclinical studies demonstrate that toxin treatment induces tumor regression in several in vivo ovarian cancer models, including patient-derived xenografts, without adverse side effects, supportive of progression toward clinical evaluation. These data lay the groundwork for developing therapeutics for treating women with late-stage and recurrent ovarian cancers, utilizing a mechanism distinct from current anticancer therapies.

Protease activity sensors enable real-time treatment response monitoring in lymphangioleiomyomatosis

BACKGROUND

Biomarkers of disease progression and treatment response are urgently needed for patients with lymphangioleiomyomatosis (LAM). Activity-based nanosensors, an emerging biosensor class, detect dysregulated proteases in vivo and release a reporter to provide a urinary readout of disease. Because proteases are dysregulated in LAM and may directly contribute to lung function decline, activity-based nanosensors may enable quantitative, real-time monitoring of LAM progression and treatment response. We aimed to assess the diagnostic utility of activity-based nanosensors in a pre-clinical model of pulmonary LAM.

METHODS

Tsc2-null cells were injected intravenously into female nude mice to establish a mouse model of pulmonary LAM. A library of 14 activity-based nanosensors, designed to detect proteases across multiple catalytic classes, was administered into the lungs of LAM mice and healthy controls, urine was collected, and mass spectrometry was performed to measure nanosensor cleavage products. Mice were then treated with rapamycin and monitored with activity-based nanosensors. Machine learning was performed to distinguish diseased from healthy and treated from untreated mice.

RESULTS

Multiple activity-based nanosensors (PP03 (cleaved by metallo, aspartic and cysteine proteases), padjusted<0.0001; PP10 (cleaved by serine, aspartic and cysteine proteases), padjusted=0.017)) were differentially cleaved in diseased and healthy lungs, enabling strong classification with a machine learning model (area under the curve (AUC) 0.95 from healthy). Within 2 days after rapamycin initiation, we observed normalisation of PP03 and PP10 cleavage, and machine learning enabled accurate classification of treatment response (AUC 0.94 from untreated).

CONCLUSIONS

Activity-based nanosensors enable noninvasive, real-time monitoring of disease burden and treatment response in a pre-clinical model of LAM.

Synthetic Circuit-Driven Expression of Heterologous Enzymes for Disease Detection

The integration of nanotechnology and synthetic biology could lay the framework for new classes of engineered biosensors that produce amplified readouts of disease states. As a proof-of-concept demonstration of this vision, here we present an engineered gene circuit that, in response to cancer-associated transcriptional deregulation, expresses heterologous enzyme biomarkers whose activity can be measured by nanoparticle sensors that generate amplified detection readouts. Specifically, we designed an AND-gate gene circuit that integrates the activity of two ovarian cancer-specific synthetic promoters to drive the expression of a heterologous protein output, secreted Tobacco Etch Virus (TEV) protease, exclusively from within tumor cells. Nanoparticle probes were engineered to carry a TEV-specific peptide substrate in order to measure the activity of the circuit-generated enzyme to yield amplified detection signals measurable in the urine or blood. We applied our integrated sense-and-respond system in a mouse model of disseminated ovarian cancer, where we demonstrated measurement of circuit-specific TEV protease activity both in vivo using exogenously administered nanoparticle sensors and ex vivo using quenched fluorescent probes. We envision that this work will lay the foundation for how synthetic biology and nanotechnology can be meaningfully integrated to achieve next-generation engineered biosensors.

Novel Ex Vivo Zymography Approach for Assessment of Protease Activity in Tissues with Activatable Antibodies

Proteases are involved in the control of numerous physiological processes, and their dysregulation has been identified in a wide range of pathologies, including cancer. Protease activity is normally tightly regulated post-translationally and therefore cannot be accurately estimated based on mRNA or protein expression alone. While several types of zymography approaches to estimate protease activity exist, there remains a need for a robust and reliable technique to measure protease activity in biological tissues. We present a novel quantitative ex vivo zymography (QZ) technology based on Probody^® therapeutics (Pb-Tx), a novel class of protease-activated cancer therapeutics that contain a substrate linker cleavable by tumor-associated proteases. This approach enables the measurement and comparison of protease activity in biological tissues via the detection of Pb-Tx activation. By exploiting substrate specificity and selectivity, cataloguing and differentiating protease activities is possible, with further refinement achieved using protease-specific inhibitors. Using the QZ assay and human tumor xenografts, patient tumor tissues, and patient plasma, we characterized protease activity in preclinical and clinical samples. The QZ assay offers the potential to increase our understanding of protease activity in tissues and inform diagnostic and therapeutic development for diseases, such as cancer, that are characterized by dysregulated proteolysis.