Reconstructed signaling and regulatory networks identify potential drugs for SARS-CoV-2 infection.
COVID-19
data integration
dynamic networks
Journal
bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187
Informations de publication
Date de publication:
09 Dec 2021
09 Dec 2021
Historique:
pubmed:
22
10
2020
medline:
22
10
2020
entrez:
21
10
2020
Statut:
epublish
Résumé
Several molecular datasets have been recently compiled to characterize the activity of SARS-CoV-2 within human cells. Here we extend computational methods to integrate several different types of sequence, functional and interaction data to reconstruct networks and pathways activated by the virus in host cells. We identify key proteins in these networks and further intersect them with genes differentially expressed at conditions that are known to impact viral activity. Several of the top ranked genes do not directly interact with virus proteins. We experimentally tested treatments for a number of the predicted targets. We show that blocking one of the predicted indirect targets significantly reduces viral loads in stem cell-derived alveolar epithelial type II cells (iAT2s).
Identifiants
pubmed: 33083801
doi: 10.1101/2020.06.01.127589
pmc: PMC7574259
pii:
doi:
Types de publication
Preprint
Langues
eng
Subventions
Organisme : NCATS NIH HHS
ID : U01 TR001810
Pays : United States
Organisme : NIAID NIH HHS
ID : UC7 AI095321
Pays : United States
Organisme : NHLBI NIH HHS
ID : T32 HL007035
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM122096
Pays : United States
Organisme : NHLBI NIH HHS
ID : 75N92020C00005
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001430
Pays : United States
Déclaration de conflit d'intérêts
Conflict of interest None.
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