Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing

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Date
2021Type
- Journal Article
Citations
Cited 13 times in
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Cited 22 times in
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ETH Bibliography
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Abstract
Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs. Show more
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https://doi.org/10.3929/ethz-b-000473487Publication status
publishedExternal links
Journal / series
Nature CommunicationsVolume
Pages / Article No.
Publisher
Nature Publishing GroupMore
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Citations
Cited 13 times in
Web of Science
Cited 22 times in
Scopus
ETH Bibliography
yes
Altmetrics