Biology subjects

Wang, C. S.

Publications and source records attributed to Wang, C. S..

5 recordsLinked to original sources

bioRxiv2026

A generalizable system for antigenic peptide targeting across HLA-I allotypes

T cell receptors (TCRs) and TCR-mimicking antibodies recognize peptide antigens in the context of specific Human Leucocyte Antigen (HLA-I) allotypes, and the extreme polymorphism of the HLA locus limits the breadth of immunotherapy development. Key barriers include divergent molecular surfaces on HLA proteins and differences in the peptide structure. As a result, existing modalities cannot confer therapeutic coverage across patients of divergent genetic backgrounds. Here, we develop an approach which combines a peptide conformational prediction tool, PepPred, with a cross-HLA binding protein engineering system, TRACeR-I1, to outline a generalized framework for developing binders (xTRACeRs) with compatibility across HLA allotypes while maintaining high levels of specificity towards the peptide antigen. We use our system to develop and validate xTRACeRs against clinically relevant, established peptide antigens presented across common alleles within five HLA-A/B/C supertypes2. Cryo-EM structures of xTRACeR-pHLA complexes for an oncofetal antigen from PRAME and a neuroblastoma-specific peptide from PHOX2B reveal effective mechanisms to navigate polymorphic HLA surface residues, and extensive interactions with the peptide. We implemented these two xTRACeRs as Chimeric Antigen Receptor (CAR) T cells and demonstrated their potent killing efficacy and specificity. Overcoming restriction across HLA supertypes lifts a key barrier in HLA-targeted immunotherapy by expanding patient coverage.

synthetic biology↗

bioRxiv2025

Multimodal profiling of proinflammatory protease activity identifies caspase-1 as a target for lung cancer interception

The systemic inhibition of IL-1b, a key mediator of pulmonary inflammation, has been shown to reduce the incidence of lung cancer in patients in years following treatment, but knowledge gaps surrounding its activation and role in the tumor microenvironment are hindering approaches for cancer interception. We developed a suite of activity-based technologies to probe inflammation in early lung cancer and identified a translational target candidate. We designed probes sensitive to various IL-1b-activating proteases and applied them to a murine model of inflammatory lung cancer, Kras/Trp53-mutant with SIINFEKL expression (KPS). Our nanosensors revealed reduced cleavage of a caspase-1 reporter in the lungs of KPS mice treated with IL-1b antibody, as well as elevated caspase-1 expression and activity in naive tumor tissue sections, highlighting the importance of caspase-1 processing of IL-1b during cancer development. We conducted a pre-clinical trial of a novel combination intervention by administering both IL-1b blockade and caspase-1 inhibition shortly after tumor induction. Following treatment, we observed significant reduction in lung cancer formation, including complete ablation of tumor incidence in nearly 20% of KPS mice. Our approach to understand the interplay of protease activity and cytokine activation supports development of new strategies to mitigate inflammation and intercept lung cancer progression.

bioengineering↗

bioRxiv2024

Engineering Multiplexed Synthetic Breath Biomarkers as Diagnostic Probes

Breath biopsy is emerging as a rapid and non-invasive diagnostic tool that links exhaled chemical signatures with specific medical conditions. Despite its potential, clinical translation remains limited by the challenge of reliably detecting endogenous, disease-specific biomarkers in breath. Synthetic biomarkers represent an emerging paradigm for precision diagnostics such that they amplify activity-based biochemical signals associated with disease fingerprints. However, their adaptation to breath biopsy has been constrained by the limited availability of orthogonal volatile reporters that are detectable in exhaled breath. Here, we engineer multiplexed breath biomarkers that couple aberrant protease activities to exogenous volatile reporters. We designed novel intramolecular reactions that leverage protease-mediated aminolysis, enabling the sensing of a broad spectrum of proteases, and that each release a unique reporter in breath. This approach was validated in a mouse model of influenza to establish baseline sensitivity and specificity in a controlled inflammatory setting and subsequently applied to diagnose lung cancer using an autochthonous Alk-mutant model. We show that combining multiplexed reporter signals with machine learning algorithms enables tumor progression tracking, treatment response monitoring, and detection of relapse after 30 minutes. Our multiplexed breath biopsy platform highlights a promising avenue for rapid, point-of-care diagnostics across diverse disease states.

bioengineering↗

bioRxiv2022

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 datasets. 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 datasets 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 can 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 towards standardizing data analysis across the field and in chemical biology and biochemistry at large.

bioinformatics↗

bioRxiv2021

Multiscale profiling of enzyme activity in cancer

Diverse processes in cancer are mediated by enzymes, which most proximally exert their function through their activity. Methods to quantify enzyme activity, rather than just expression, are therefore critical to our ability to understand the pathological roles of enzymes in cancer and to harness this class of biomolecules as diagnostic and therapeutic targets. Here we present an integrated set of methods for measuring specific enzyme activities across the organism, tissue, and cellular levels, which we unify into a methodological hierarchy to facilitate biological discovery. We focus on proteases for method development and validate our approach through the study of tumor progression and treatment response in an autochthonous model of Alk-mutant lung cancer. To quantitatively measure activity dynamics over time, we engineered multiplexed, peptide-based nanosensors to query protease activity in vivo. Machine learning analysis of sensor measurements revealed dramatic protease dysregulation in lung cancer, including significantly enhanced proteolytic cleavage of one peptide, S1 (Padj < 0.0001), which returned to healthy levels within three days after initiation of targeted therapy. Next, to link these organism-level observations to the in situ context, we established a multiplexed assay for on-tissue localization of enzyme activity and pinpointed S1 cleavage to endothelial cells and pericytes of the tumor vasculature. Lastly, to directly link enzyme activity measurements to cellular phenotype, we designed a high-throughput method to isolate and characterize proteolytically active cells, uncovering profound upregulation of pro-angiogenic transcriptional programs in S1-positive cells. Together, these methods allowed us to discover that protease production by angiogenic vasculature responds rapidly to targeted therapy against oncogene-addicted tumor cells, identifying a highly dynamic interplay between tumor cells and their microenvironment. This work provides a generalizable framework to functionally characterize enzyme activity in cancer.

bioengineering↗