Dataset Information

A high-throughput cigarette smoke-treated bronchosphere model for disease-relevant phenotypic compound screening.

ABSTRACT: Cigarette smoking (CS) is the leading cause of COPD, and identifying the pathways that are driving pathogenesis in the airway due to CS exposure can aid in the discovery of novel therapies for COPD. An additional barrier to the identification of key pathways that are involved in the CS-induced pathogenesis is the difficulty in building relevant and high throughput models that can recapitulate the phenotypic and transcriptomic changes associated with CS exposure. To identify these drivers, we have developed a cigarette smoke extract (CSE)-treated bronchosphere assay in 384-well plate format that exhibits CSE-induced decreases in size and increase in luminal secretion of MUC5AC. Transcriptomic changes in CSE-treated bronchospheres resemble changes that occur in human smokers both with and without COPD compared to healthy groups, indicating that this model can capture human smoking signature. To identify new targets, we ran a small molecule compound deck screening with diversity in target mechanisms of action and identified hit compounds that attenuated CSE induced changes, either decreasing spheroid size or increasing secreted mucus. This work provides insight into the utility of this bronchopshere model to examine human respiratory disease impacted by CSE exposure and the ability to screen for therapeutics to reverse the pathogenic changes caused by CSE.

SUBMITTER: Beri P

PROVIDER: S-EPMC10310037 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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A high-throughput cigarette smoke-treated bronchosphere model for disease-relevant phenotypic compound screening.

Beri Pranjali P Woo Young Jae YJ Schierenbeck Katie K Chen Kaisheng K Barnes S Whitney SW Ross Olivia O Krutil Douglas D Quackenbush Doug D Fang Bin B Walker John J Barnes William W Toyama Erin Quan EQ

PloS one 20230629 6

Cigarette smoking (CS) is the leading cause of COPD, and identifying the pathways that are driving pathogenesis in the airway due to CS exposure can aid in the discovery of novel therapies for COPD. An additional barrier to the identification of key pathways that are involved in the CS-induced pathogenesis is the difficulty in building relevant and high throughput models that can recapitulate the phenotypic and transcriptomic changes associated with CS exposure. To identify these drivers, we hav ...[more]

PMID: 37384771

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Project description:Target-based high-throughput compound screening dominates conventional one-drug-one-gene drug discovery process. However, the readout from the chemical modulation of a single protein is poorly correlated with phenotypic response of organism, leading to high failure rate in drug development. Chemical-induced gene expression profile provides an attractive solution to phenotype-based screening. However, the use of such data is currently limited by their sparseness, unreliability, and relatively low throughput. Several methods have been proposed to impute missing values for gene expression datasets. However, few existing methods can perform de novo chemical compound screening. In this study, we propose a mechanism-driven neural network-based method named DeepCE (Deep Chemical Expression) which utilizes graph convolutional neural network to learn chemical representation and multi-head attention mechanism to model chemical substructure-gene and gene-gene feature associations. In addition, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves the superior performances not only in de novo chemical setting but also in traditional imputation setting compared to state-of-the-art baselines for the prediction of chemical-induced gene expression. We further verify the effectiveness of gene expression profiles generated from DeepCE by comparing them with gene expression profiles in L1000 dataset for downstream classification tasks including drug-target and disease predictions. To demonstrate the value of DeepCE, we apply it to patient-specific drug repurposing of COVID-19 for the first time, and generate novel lead compounds consistent with clinical evidences. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data as well as screening novel chemicals for the modulation of systemic response to disease.

Dataset Information

A high-throughput cigarette smoke-treated bronchosphere model for disease-relevant phenotypic compound screening.

Publications

A high-throughput cigarette smoke-treated bronchosphere model for disease-relevant phenotypic compound screening.

Similar Datasets

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