bioRxivSystems BiologyPreprint
The Host Cell ViroCheckpoint: Identification and Pharmacologic Targeting of Novel Mechanistic Determinants of Coronavirus-Mediated Hijacked Cell States
Pasquale LaiseiD, Gideon Bosker, Xiaoyun SuniD, Yao SheniD, Eugene F. DouglassiD, Charles KaraniD, Ronald B. RealubitiD, Sergey PampouiD, Andrea CalifanoiD, Mariano J. AlvareziD
Abstract
Most antiviral agents are designed to target virus-specific proteins and mechanisms rather than the host cell proteins that are critically dysregulated following virus-mediated reprogramming of the host cell transcriptional state. To overcome these limitations, we propose that elucidation and pharmacologic targeting of host cell Master Regulator proteins--whose aberrant activities govern the reprogramed state of infected-coronavirus cells--presents unique opportunities to develop novel mechanism-based therapeutic approaches to antiviral therapy, either as monotherapy or as a complement to established treatments. Specifically, we propose that a small module of host cell Master Regulator proteins (ViroCheckpoint) is hijacked by the virus to support its efficient replication and release. Conventional methodologies are not well suited to elucidate these potentially targetable proteins. By using the VIPER network-based algorithm, we successfully interrogated 12h, 24h, and 48h signatures from Calu-3 lung adenocarcinoma cells infected with SARS-CoV, to elucidate the time-dependent reprogramming of host cells and associated Master Regulator proteins. We used the NYS CLIA-certified Darwin OncoTreat algorithm, with an existing database of RNASeq profiles following cell perturbation with 133 FDA-approved and 195 late-stage experimental compounds, to identify drugs capable of virtually abrogating the virus-induced Master Regulator signature. This approach to drug prioritization and repurposing can be trivially extended to other viral pathogens, including SARS-CoV-2, as soon as the relevant infection signature becomes available.
- organism: human
- assay: bulk RNA-seq
- research focus: drug response / drug discovery
- research focus: differential expression
- research focus: benchmarking
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 16
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