Host protease activity classifies pneumonia etiology

An Introductory Guide to Protease Sensitive Linker Design Using Matrix Metalloproteinase 13 as an Example

Proteases play a crucial role, not only in physiological, but also in pathological processes, such as cancer, inflammation, arthritis, Alzheimer's, and infections, to name but a few. Their ability to cleave peptides can be harnessed for a broad range of biotechnological purposes. To do this efficiently, it is essential to find an amino acid sequence that meets the necessary requirements, including interdependent factors like specificity, selectivity, cleavage kinetics, or synthetic accessibility. Cleavage sequences from natural substrates of the protease may not be optimal in terms of specificity and selectivity, which is why these frequently require arduous and sometimes unsuccessful optimization such as by iterative exchange of single amino acids. Hence, here we describe the systematic design of protease sensitive linkers (PSLs)─peptide sequences specifically cleaved by a target protease─guided by the mass spectrometry based determination of target protease specific cleavage sites from a proteome-based peptide library. It includes a procedure for identifying bespoke PSL sequences, their optimization, synthesis, and validation and introduces a program that can indicate potential cleavage sites by hundreds of enzymes in any arbitrary amino acid sequence. Thereby, we provide an introduction to PSL design, illustrated by the example of matrix metalloproteinase 13 (MMP13). This introduction can serve as a guide and help to greatly accelerate the development and use of protease-sensitive linkers in diverse applications.

Targeting and monitoring ovarian cancer invasion with an RNAi and peptide delivery system

RNA interference (RNAi) therapeutics are an emerging class of medicines that selectively target mRNA transcripts to silence protein production and combat disease. Despite the recent progress, a generalizable approach for monitoring the efficacy of RNAi therapeutics without invasive biopsy remains a challenge. Here, we describe the development of a self-reporting, theranostic nanoparticle that delivers siRNA to silence a protein that drives cancer progression while also monitoring the functional activity of its downstream targets. Our therapeutic target is the transcription factor SMARCE1, which was previously identified as a key driver of invasion in early-stage breast cancer. Using a doxycycline-inducible shRNA knockdown in OVCAR8 ovarian cancer cells both in vitro and in vivo, we demonstrate that SMARCE1 is a master regulator of genes encoding proinvasive proteases in a model of human ovarian cancer. We additionally map the peptide cleavage profiles of SMARCE1-regulated proteases so as to design a readout for downstream enzymatic activity. To demonstrate the therapeutic and diagnostic potential of our approach, we engineered self-assembled layer-by-layer nanoparticles that can encapsulate nucleic acid cargo and be decorated with peptide substrates that release a urinary reporter upon exposure to SMARCE1-related proteases. In an orthotopic ovarian cancer xenograft model, theranostic nanoparticles were able to knockdown SMARCE1 which was in turn reported through a reduction in protease-activated urinary reporters. These LBL nanoparticles both silence gene products by delivering siRNA and noninvasively report on downstream target activity by delivering synthetic biomarkers to sites of disease, enabling dose-finding studies as well as longitudinal assessments of efficacy.

Inhalable point-of-care urinary diagnostic platform

Although low-dose computed tomography screening improves lung cancer survival in at-risk groups, inequality remains in lung cancer diagnosis due to limited access to and high costs of medical imaging infrastructure. We designed a needleless and imaging-free platform, termed PATROL (point-of-care aerosolizable nanosensors with tumor-responsive oligonucleotide barcodes), to reduce resource disparities for early detection of lung cancer. PATROL formulates a set of DNA-barcoded, activity-based nanosensors (ABNs) into an inhalable format. Lung cancer-associated proteases selectively cleave the ABNs, releasing synthetic DNA reporters that are eventually excreted via the urine. The urinary signatures of barcoded nanosensors are quantified within 20 min at room temperature using a multiplexable paper-based lateral flow assay. PATROL detects early-stage tumors in an autochthonous lung adenocarcinoma mouse model with high sensitivity and specificity. Tailoring the library of ABNs may enable not only the modular PATROL platform to lower the resource threshold for lung cancer early detection tools but also the rapid detection of chronic pulmonary disorders and infections.

COVID-19 on Oral Health: A New Bilateral Connection for the Pandemic

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and transmission are generally known to be produced by respiratory droplets and aerosols from the oral cavity (O.C.) of infected subjects, as stated by the World Health Organization. Saliva also retains the viral particles and aids in the spread of COVID-19. Angiotensin-converting enzyme Type 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) are two of the numerous factors that promote SARS-CoV-2 infection, expressed by O.C. structures, various mucosa types, and the epithelia of salivary glands. A systemic SARS-CoV-2 infection might result from viral replication in O.C. cells. On the other hand, cellular damage of different subtypes in the O.C. might be associated with various clinical signs and symptoms. Factors interfering with SARS-CoV-2 infection potential might represent fertile ground for possible local pharmacotherapeutic interventions, which may confine SARS-CoV-2 virus entry and transmission in the O.C., finally representing a way to reduce COVID-19 incidence and severity.

A Breath-Based In Vitro Diagnostics for Lower Respiratory Tract Infection

Lower Respiratory Tract Infections (LRTIs) represent the leading cause of death due to infectious diseases. Current diagnostic modalities primarily depend on clinical symptoms and lack specificity, especially in light of common colonization without overt infection. To address this, we developed a noninvasive diagnostic approach that employs BreathBiomics™, an advanced human breath sampling system, to detect protease activities induced by bacterial infection in the lower respiratory tract. Specifically, we engineered a high-sensitivity and high-specificity molecular sensor for human neutrophil elastase (HNE). The sensor undergoes cleavage in the presence of HNE, an event that is subsequently detected via Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS). Application of this methodology to clinical samples, breath specimens collected from intubated patients with LRTIs, demonstrated the detection of the cleaved sensor by MALDI-TOF MS. Our findings indicate that this novel approach offers a noninvasive and specific diagnostic strategy for people with LRTIs.

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.