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.

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.

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.

Ionic Liquid-Mediated Transdermal Delivery of Thrombosis-Detecting Nanosensors.

Blood clotting disorders such as pulmonary embolism are associated with high morbidity and mortality. A large portion of thrombotic events occur postoperative and after hospital discharge. Therefore, easily applicable, noninvasive, and long-term monitoring of thrombosis occurrence is critical for urgent clinical intervention. Here, the use is proposed of ionic liquids as a skin transport facilitator to deliver thrombin-sensitive nanosensors that enable prolonged monitoring of pulmonary embolism. Co-formulation of nanosensors with choline and geranic acid (CAGE) ionic liquids demonstrates significant transdermal diffusion into the dermis of the skin and provides sustained release into the blood throughout 72 h. Upon reaching the systemic circulation, the nanosensors release reporter molecules into the urine by responding to activation of the clotting cascade and retain a diagnostic power for 24 h in an acute pulmonary embolism mouse model. These results demonstrate a proof-of-concept disease monitoring system that can be topically applied by patients and potentially reduce mortality and high cost of hospitalization.

Evidential Deep Learning for Guided Molecular Property Prediction and Discovery.

While neural networks achieve state-of-the-art performance for many molecular modeling and structure-property prediction tasks, these models can struggle with generalization to out-of-domain examples, exhibit poor sample efficiency, and produce uncalibrated predictions. In this paper, we leverage advances in evidential deep learning to demonstrate a new approach to uncertainty quantification for neural network-based molecular structure-property prediction at no additional computational cost. We develop both evidential 2D message passing neural networks and evidential 3D atomistic neural networks and apply these networks across a range of different tasks. We demonstrate that evidential uncertainties enable (1) calibrated predictions where uncertainty correlates with error, (2) sample-efficient training through uncertainty-guided active learning, and (3) improved experimental validation rates in a retrospective virtual screening campaign. Our results suggest that evidential deep learning can provide an efficient means of uncertainty quantification useful for molecular property prediction, discovery, and design tasks in the chemical and physical sciences.

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.

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

Recent years have seen the emergence of conditionally activated diagnostics and therapeutics that leverage protease-cleavable peptide linkers to enhance their specificity for cancer. However, due to a lack of methods to measure and localize protease activity directly within the tissue microenvironment, the design of protease-activated agents has been necessarily empirical, yielding suboptimal results when translated to patients. To address the need for spatially resolved protease activity profiling in cancer, we developed a new class of in situ probes that can be applied to fresh-frozen tissue sections in a manner analogous to immunofluorescence staining. These activatable zymography probes (AZP) detected dysregulated protease activity in human prostate cancer biopsy samples, enabling disease classification. AZPs were leveraged within a generalizable framework to design conditional cancer diagnostics and therapeutics and showcased in the Hi-Myc mouse model of prostate cancer, which models features of early pathogenesis. Multiplexed screening against barcoded substrates yielded a peptide, S16, that was robustly and specifically cleaved by tumor-associated metalloproteinases in the Hi-Myc model. In situ labeling with an AZP incorporating S16 revealed a potential role of metalloproteinase dysregulation in proliferative, premalignant Hi-Myc prostatic glands. Systemic administration of an in vivo imaging probe incorporating S16 perfectly classified diseased and healthy prostates, supporting the relevance of ex vivo activity assays to in vivo translation. We envision AZPs will enable new insights into the biology of protease dysregulation in cancer and accelerate the development of conditional diagnostics and therapeutics for multiple cancer types. SIGNIFICANCE: Visualization of protease activity within the native tissue context using AZPs provides new biological insights into protease dysregulation in cancer and guides the design of conditional diagnostics and therapeutics.

Activity-Based Diagnostics: An Emerging Paradigm for Disease Detection and Monitoring.

Diagnostics to accurately detect disease and monitor therapeutic response are essential for effective clinical management. Bioengineering, chemical biology, molecular biology, and computer science tools are converging to guide the design of diagnostics that leverage enzymatic activity to measure or produce biomarkers of disease. We review recent advances in the development of these 'activity-based diagnostics' (ABDx) and their application in infectious and noncommunicable diseases. We highlight efforts towards both molecular probes that respond to disease-specific catalytic activity to produce a diagnostic readout, as well as diagnostics that use enzymes as an engineered component of their sense-and-respond cascade. These technologies exemplify how integrating techniques from multiple disciplines with preclinical validation has enabled ABDx that may realize the goals of precision medicine.

Genetic Encoding of Targeted Magnetic Resonance Imaging Contrast Agents for Tumor Imaging.

Tumor-selective contrast agents have the potential to aid in the diagnosis and treatment of cancer using noninvasive imaging modalities such as magnetic resonance imaging (MRI). Such contrast agents can consist of magnetic nanoparticles incorporating functionalities that respond to cues specific to tumor environments. Genetically engineering magnetotactic bacteria to display peptides has been investigated as a means to produce contrast agents that combine the robust image contrast effects of magnetosomes with the transgenic-targeting peptides displayed on their surface. This work reports the first use of magnetic nanoparticles that display genetically encoded pH low insertion peptide (pHLIP), a long peptide intended to enhance MRI contrast by targeting the extracellular acidity associated with the tumors. To demonstrate the modularity of this versatile platform to incorporate diverse targeting ligands by genetic engineering, we also incorporated the cyclic αv integrin-binding peptide iRGD into separate magnetosomes. Specifically, we investigate their potential for enhanced binding and tumor imaging both in vitro and in vivo. Our experiments indicate that these tailored magnetosomes retain their magnetic properties, making them well suited as T2 contrast agents, while exhibiting an increased binding compared to the binding in wild-type magnetosomes.

Pharmacokinetic tuning of protein-antigen fusions enhances the immunogenicity of T-cell vaccines.

The formulations of peptide-based antitumour vaccines being tested in clinical studies are generally associated with weak potency. Here, we show that pharmacokinetically tuning the responses of peptide vaccines by fusing the peptide epitopes to carrier proteins optimizes vaccine immunogenicity in mice. In particular, we show in immunized mice that the carrier protein transthyretin simultaneously optimizes three factors: efficient antigen uptake in draining lymphatics from the site of injection, protection of antigen payloads from proteolytic degradation and reduction of antigen presentation in uninflamed distal lymphoid organs. Optimizing these factors increases vaccine immunogenicity by up to 90-fold and maximizes the responses to viral antigens, tumour-associated antigens, oncofetal antigens and shared neoantigens. Protein-peptide epitope fusions represent a facile and generalizable strategy for enhancing the T-cell responses elicited by subunit vaccines.

Urinary detection of lung cancer in mice via noninvasive pulmonary protease profiling.

Lung cancer is the leading cause of cancer-related death, and patients most commonly present with incurable advanced-stage disease. U.S. national guidelines recommend screening for high-risk patients with low-dose computed tomography, but this approach has limitations including high false-positive rates. Activity-based nanosensors can detect dysregulated proteases in vivo and release a reporter to provide a urinary readout of disease activity. Here, we demonstrate the translational potential of activity-based nanosensors for lung cancer by coupling nanosensor multiplexing with intrapulmonary delivery and machine learning to detect localized disease in two immunocompetent genetically engineered mouse models. The design of our multiplexed panel of sensors was informed by comparative transcriptomic analysis of human and mouse lung adenocarcinoma datasets and in vitro cleavage assays with recombinant candidate proteases. Intrapulmonary administration of the nanosensors to a Kras- and Trp53-mutant lung adenocarcinoma mouse model confirmed the role of metalloproteases in lung cancer and enabled accurate detection of localized disease, with 100% specificity and 81% sensitivity. Furthermore, this approach generalized to an alternative autochthonous model of lung adenocarcinoma, where it detected cancer with 100% specificity and 95% sensitivity and was not confounded by lipopolysaccharide-driven lung inflammation. These results encourage the clinical development of activity-based nanosensors for the detection of lung cancer.

Renal clearable catalytic gold nanoclusters for in vivo disease monitoring.

Ultrasmall gold nanoclusters (AuNCs) have emerged as agile probes for in vivo imaging, as they exhibit exceptional tumour accumulation and efficient renal clearance properties. However, their intrinsic catalytic activity, which can enable an increased detection sensitivity, has yet to be explored for in vivo sensing. By exploiting the peroxidase-mimicking activity of AuNCs and the precise nanometre-size filtration of the kidney, we designed multifunctional protease nanosensors that respond to disease microenvironments to produce a direct colorimetric urinary readout of the disease state in less than one hour. We monitored the catalytic activity of AuNCs in the collected urine of a mouse model of colorectal cancer in which tumour-bearing mice showed a 13-fold increase in colorimetric signal compared to healthy mice. The nanosensors were eliminated completely through hepatic and renal excretion within four weeks of injection with no evidence of toxicity. We envision that this modular approach will enable the rapid detection of a diverse range of diseases by exploiting their specific enzymatic signatures.

Synthetic recombinase-based state machines in living cells.

State machines underlie the sophisticated functionality behind human-made and natural computing systems that perform order-dependent information processing. We developed a recombinase-based framework for building state machines in living cells by leveraging chemically controlled DNA excision and inversion operations to encode states in DNA sequences. This strategy enables convenient readout of states (by sequencing and/or polymerase chain reaction) as well as complex regulation of gene expression. We validated our framework by engineering state machines in Escherichia coli that used one, two, or three chemical inputs to control up to 16 DNA states. These state machines were capable of recording the temporal order of all inputs and performing multi-input, multi-output control of gene expression. We also developed a computational tool for the automated design of gene regulation programs using recombinase-based state machines. Our scalable framework should enable new strategies for recording and studying how combinational and temporal events regulate complex cell functions and for programming sophisticated cell behaviors.