Biology subjects

Brooks, C. A.

Publications and source records attributed to Brooks, C. A..

3 recordsLinked to original sources

bioRxiv2025

A phenotypic screen identified KEAP1-kelch domain blockade as a mechanism to restore cardiac function in the setting of chronic severe hemodynamic stress

A bespoke gene expression (GE) based small molecule screen in human induced pluripotent stem cell derived cardiomyocytes (iPSC-CM) was conducted with the aim of identifying drug targets and pathways with the potential to reverse heart failure (HF) pathologic GE and the resultant decompensated HF phenotype. The screen utilized a composite human-murine HF gene expression signature, a target annotated compound set, and a HF gene expression reversal scoring algorithm to identify small molecules and their associated targets as potential modulators of HF pathologic GE. Following hit triage, a lead optimization program, and compound characterization in preclinical rodent models of HF and human iPSC-CM contractility assays, KEAP1 kelch domain blockers were identified as potent efficacious agents in the restoration of contractile function in the setting of oxidant stress and pressure overload induced cardiac dysfunction.

molecular biology↗

bioRxiv2024

Profiling endogenous airway proteases and antiproteases and measuring proteolytic activation of Influenza HA using in vitro and ex vivo human airway surface liquid samples

Imbalance of airway proteases and antiproteases has been implicated in diseases such as COPD and environmental exposures including cigarette smoke and ozone. To initiate infection, endogenous proteases are commandeered by respiratory viruses upon encountering the airway epithelium. The airway proteolytic environment likely contains redundant antiproteases and proteases with diverse catalytic mechanisms, however a proteomic profile of these enzymes and inhibitors in airway samples has not been reported. The objective of this study was to first profile extracellular proteases and antiproteases using human airway epithelial cell cultures and ex vivo nasal epithelial lining fluid (NELF) samples. Secondly, we present an optimized method for probing the proteolytic environment of airway surface liquid samples (in vitro and ex vivo) using fluorogenic peptides modeling the cleavage sites of respiratory viruses. We detected 48 proteases in the apical wash of cultured human nasal epithelial cells (HNECs) (n=6) and 57 in NELF (n=13) samples from healthy human subjects using mass-spectrometry based proteomics. Additionally, we detected 29 and 48 antiproteases in the HNEC apical washes and NELF, respectively. We observed large interindividual variability in rate of cleavage of an Influenza H1 peptide in the ex vivo clinical samples. Since protease and antiprotease levels have been found to be altered in the airways of smokers, we compared proteolytic cleavage in ex vivo nasal lavage samples from male/female smokers and non-smokers. There was a statistically significant increase in proteolysis of Influenza H1 in NLF from male smokers compared to female smokers. Furthermore, we measured cleavage of the S1/S2 site of SARS-CoV, SARS-CoV-2, and SARS-CoV-2 Delta peptides in various airway samples, suggesting the method could be used for other viruses of public health relevance. This assay presents a direct and efficient method of evaluating the proteolytic environment of human airway samples in assessment of therapeutic treatment, exposure, or underlying disease.

immunology↗

bioRxiv2020

Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery

Development of accurate disease models and discovery of immune-modulating drugs is challenged by the immune systems highly interconnected and context-dependent nature. Here we apply deep-learning-driven analysis of cellular morphology to develop a scalable "phenomics" platform and demonstrate its ability to identify dose-dependent, high-dimensional relationships among and between immunomodulators, toxins, pathogens, genetic perturbations, and small and large molecules at scale. High-throughput screening on this platform demonstrates rapid identification and triage of hits for TGF-{beta}- and TNF--driven phenotypes. We deploy the platform to develop phenotypic models of active SARS-CoV-2 infection and of COVID-19-associated cytokine storm, surfacing compounds with demonstrated clinical benefit and identifying several new candidates for drug repurposing. The presented library of images, deep learning features, and compound screening data from immune profiling and COVID-19 screens serves as a deep resource for immune biology and cellular-model drug discovery with immediate impact on the COVID-19 pandemic.

immunology↗