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.

Host protease activity classifies pneumonia etiology.

Community-acquired pneumonia (CAP) has been brought to the forefront of global health priorities due to the COVID-19 pandemic. However, classification of viral versus bacterial pneumonia etiology remains a significant clinical challenge. To this end, we have engineered a panel of activity-based nanosensors that detect the dysregulated activity of pulmonary host proteases implicated in the response to pneumonia-causing pathogens and produce a urinary readout of disease. The nanosensor targets were selected based on a human protease transcriptomic signature for pneumonia etiology generated from 33 unique publicly available study cohorts. Five mouse models of bacterial or viral CAP were developed to assess the ability of the nanosensors to produce etiology-specific urinary signatures. Machine learning algorithms were used to train diagnostic classifiers that could distinguish infected mice from healthy controls and differentiate those with bacterial versus viral pneumonia with high accuracy. This proof-of-concept diagnostic approach demonstrates a way to distinguish pneumonia etiology based solely on the host proteolytic response to infection.

Engineering synthetic breath biomarkers for respiratory disease.

Human breath contains many volatile metabolites. However, few breath tests are currently used in the clinic to monitor disease due to bottlenecks in biomarker identification. Here we engineered breath biomarkers for respiratory disease by local delivery of protease-sensing nanoparticles to the lungs. The nanosensors shed volatile reporters upon cleavage by neutrophil elastase, an inflammation-associated protease with elevated activity in lung diseases such as bacterial infection and alpha-1 antitrypsin deficiency. After intrapulmonary delivery into mouse models with acute lung inflammation, the volatile reporters are released and expelled in breath at levels detectable by mass spectrometry. These breath signals can identify diseased mice with high sensitivity as early as 10 min after nanosensor administration. Using these nanosensors, we performed serial breath tests to monitor dynamic changes in neutrophil elastase activity during lung infection and to assess the efficacy of a protease inhibitor therapy targeting neutrophil elastase for the treatment of alpha-1 antitrypsin deficiency.

Amygdala inputs to prefrontal cortex guide behavior amid conflicting cues of reward and punishment.

Orchestrating appropriate behavioral responses in the face of competing signals that predict either rewards or threats in the environment is crucial for survival. The basolateral nucleus of the amygdala (BLA) and prelimbic (PL) medial prefrontal cortex have been implicated in reward-seeking and fear-related responses, but how information flows between these reciprocally connected structures to coordinate behavior is unknown. We recorded neuronal activity from the BLA and PL while rats performed a task wherein competing shock- and sucrose-predictive cues were simultaneously presented. The correlated firing primarily displayed a BLA→PL directionality during the shock-associated cue. Furthermore, BLA neurons optogenetically identified as projecting to PL more accurately predicted behavioral responses during competition than unidentified BLA neurons. Finally photostimulation of the BLA→PL projection increased freezing, whereas both chemogenetic and optogenetic inhibition reduced freezing. Therefore, the BLA→PL circuit is critical in governing the selection of behavioral responses in the face of competing signals.

Gene expression-based discovery of atovaquone as a STAT3 inhibitor and anticancer agent.

The oncogenic transcription factor signal transducer and activator of transcription 3 (STAT3) is frequently activated inappropriately in a wide range of hematological and solid cancers, but clinically available therapies targeting STAT3 are lacking. Using a computational strategy to identify compounds opposing the gene expression signature of STAT3, we discovered atovaquone (Mepron), an antimicrobial approved by the US Food and Drug Administration, to be a potent STAT3 inhibitor. We show that, at drug concentrations routinely achieved clinically in human plasma, atovaquone inhibits STAT3 phosphorylation, the expression of STAT3 target genes, and the viability of STAT3-dependent hematological cancer cells. These effects were also observed with atovaquone treatment of primary blasts isolated from patients with acute myelogenous leukemia or acute lymphocytic leukemia. Atovaquone is not a kinase inhibitor but instead rapidly and specifically downregulates cell-surface expression of glycoprotein 130, which is required for STAT3 activation in multiple contexts. The administration of oral atovaquone to mice inhibited tumor growth and prolonged survival in a murine model of multiple myeloma. Finally, in patients with acute myelogenous leukemia treated with hematopoietic stem cell transplantation, extended use of atovaquone for Pneumocystis prophylaxis was associated with improved relapse-free survival. These findings establish atovaquone as a novel, clinically accessible STAT3 inhibitor with evidence of anticancer efficacy in both animal models and humans.

A circuit mechanism for differentiating positive and negative associations.

The ability to differentiate stimuli predicting positive or negative outcomes is critical for survival, and perturbations of emotional processing underlie many psychiatric disease states. Synaptic plasticity in the basolateral amygdala complex (BLA) mediates the acquisition of associative memories, both positive and negative. Different populations of BLA neurons may encode fearful or rewarding associations, but the identifying features of these populations and the synaptic mechanisms of differentiating positive and negative emotional valence have remained unknown. Here we show that BLA neurons projecting to the nucleus accumbens (NAc projectors) or the centromedial amygdala (CeM projectors) undergo opposing synaptic changes following fear or reward conditioning. We find that photostimulation of NAc projectors supports positive reinforcement while photostimulation of CeM projectors mediates negative reinforcement. Photoinhibition of CeM projectors impairs fear conditioning and enhances reward conditioning. We characterize these functionally distinct neuronal populations by comparing their electrophysiological, morphological and genetic features. Overall, we provide a mechanistic explanation for the representation of positive and negative associations within the amygdala.