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A comparative study of COVID-19 transcriptional signatures between clinical samples and preclinical cell models in the search for disease master regulators and drug repositioning candidates

Medicine and Health

A comparative study of COVID-19 transcriptional signatures between clinical samples and preclinical cell models in the search for disease master regulators and drug repositioning candidates

H. Chapola, M. A. D. Bastiani, et al.

This groundbreaking research by Henrique Chapola, Marco Antônio De Bastiani, Marcelo Mendes Duarte, Matheus Becker Freitas, Jussara Severo Schuster, Daiani Machado De Vargas, and Fábio Klamt uncovers critical COVID-19 master regulators and explores potential drug repositioning candidates through an innovative comparison of clinical lung autopsy samples and preclinical cell models. Discover how these findings bridge the gap between lab results and real-world applications in treating this pandemic illness.

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Playback language: English
Abstract
This study compared COVID-19 transcriptional signatures from clinical lung autopsy samples and preclinical cell models to identify disease master regulators and potential drug repositioning candidates. Differential gene expression analysis, gene ontology (GO), and master regulator (MR) analysis were performed on clinical samples, and the Connectivity Map (CMap) approach was used for drug repositioning. Publicly available expression data from SARS-CoV-2-transfected cells and nasopharyngeal swabs were analyzed to assess the correlation between preclinical models and clinical samples. The results identified key master regulators and potential drug candidates, and also investigated the similarity between different models and clinical samples.
Publisher
Virus Research
Published On
Jan 26, 2023
Authors
Henrique Chapola, Marco Antônio De Bastiani, Marcelo Mendes Duarte, Matheus Becker Freitas, Jussara Severo Schuster, Daiani Machado De Vargas, Fábio Klamt
Tags
COVID-19
transcriptional signatures
drug repositioning
gene expression
master regulators
cell models
clinical samples

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