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.