bioRxivMicrobiologyPreprint
The FDA-approved drug Alectinib compromises SARS-CoV-2 nucleocapsid phosphorylation and inhibits viral infection in vitro
T. M. Yaron, B. E. Heaton, T. M. Levy, J. L. Johnson, T. X. Jordan, B. M. Cohen, A. Kerelsky, T.-Y. Lin, K. M. Liberatore, D. K. Bulaon, E. R. Kastenhuber, M. N. Mercadante, K. Shobana-Ganesh, L. He, R. E. Schwartz, S. Chen, H. Weinstein, O. Elemento, E. Piskounova, B. E. Nilsson-Payant, G. Lee, J. D. Trimarco, K. N. Burke, C. E. Hamele, R. R. Chaparian, , M Kavanagh-Williamson
Abstract
While vaccines are vital for preventing COVID-19 infections, it is critical to develop new therapies to treat patients who become infected. Pharmacological targeting of a host factor required for viral replication can suppress viral spread with a low probability of viral mutation leading to resistance. In particular, host kinases are highly druggable targets and a number of conserved coronavirus proteins, notably the nucleoprotein (N), require phosphorylation for full functionality. In order to understand how targeting kinases could be used to compromise viral replication, we used a combination of phosphoproteomics and bioinformatics as well as genetic and pharmacological kinase inhibition to define the enzymes important for SARS-CoV-2 N protein phosphorylation and viral replication. From these data, we propose a model whereby SRPK1/2 initiates phosphorylation of the N protein, which primes for further phosphorylation by GSK-3/{beta} and CK1 to achieve extensive phosphorylation of the N protein SR-rich domain. Importantly, we were able to leverage our data to identify an FDA-approved kinase inhibitor, Alectinib, that suppresses N phosphorylation by SRPK1/2 and limits SARS-CoV-2 replication. Together, these data suggest that repurposing or developing novel host-kinase directed therapies may be an efficacious strategy to prevent or treat COVID-19 and other coronavirus-mediated diseases.
- organism: human
- assay: proteomics
- research focus: drug response / drug discovery
- approach: statistical model
- approach: experimental assay
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