Identification of novel anti-ZIKV drugs from viral-infection temporal gene expression profiles.

Zika virus (ZIKV) infections are typically asymptomatic but cause severe neurological complications (e.g. Guillain-Barré syndrome in adults, and microcephaly in newborns). There are currently no specific therapy or vaccine options available to prevent ZIKV infections. Temporal gene expression profiles of ZIKV-infected human brain microvascular endothelial cells (HBMECs) were used in this study to identify genes essential for viral replication. These genes were then used to identify novel anti-ZIKV agents and validated in publicly available data and functional wet-lab experiments. Here, we found that ZIKV effectively evaded activation of immune response-related genes and completely reprogrammed cellular transcriptional architectures. Knockdown of genes, which gradually upregulated during viral infection but showed distinct expression patterns between ZIKV- and mock infection, discovered novel proviral and antiviral factors. One-third of the 74 drugs found through signature-based drug repositioning and cross-reference with the Drug Gene Interaction Database (DGIdb) were known anti-ZIKV agents. In cellular assays, two promising antiviral candidates (Luminespib/NVP-AUY922, L-161982) were found to reduce viral replication without causing cell toxicity. Overall, our time-series transcriptome-based methods offer a novel and feasible strategy for antiviral drug discovery. Our strategies, which combine conventional and data-driven analysis, can be extended for other pathogens causing pandemics in the future.