A High-Content Pseudotime Framework for Morphological Profiling of Dengue Virus Inhibitors from a Drug-Repurposing Library.
Hoffstadt Jillian G JG, Boisvert Samantha F SF, Wotring Jesse W JW, Porter Samuel S et al.
Dengue virus (DENV) causes dengue fever, a globally prevalent, epidemic-prone disease with no FDA-approved antivirals. Additionally, the available vaccine for DENV can increase the risk of severe dengue fever for those who have never had a DENV infection due to antibody-dependent enhancement. Thus, there is an urgent need to identify DENV antivirals. Drug repurposing, high-content screening, and machine learning were leveraged to efficiently identify antivirals likely to inhibit DENV replication. Using high-content screening in a DENV infection model, we characterized the morphological patterns of NS4B and envelope (E) protein expression over time and developed a viral pseudotime model that predicts infection progression to aid in phenotypic interpretation of drug effects. We then developed a single cell infection classifier for antiviral efficacy and performed high-throughput drug screening of 960 compounds. We identified four concentration-dependent inhibitors of DENV with nanomolar-to-submicromolar potencies: esomeprazole, GW4064, pralatrexate, and LY411575. With our pseudotime machine learning model trained on data from the time-series assay, we scored the concentration-dependent inhibitors to map their mode of action onto time points in the viral life cycle. Our most notable results show that LY411575, a gamma secretase inhibitor, exhibited an IC 50 of 72 nM and reduced percent infection to near the level of the mock infection control. Pseudotime inferences generated mode of action predictions that were supported by time-of-addition experiments, indicating that LY411575 arrests DENV infection before the development of late-stage infection associated morphologies, while retaining activity when added after inoculation. Our approach reported herein couples high-content screening and a viral pseudotime model as a robust platform for the identification of viral inhibitors with imputation of morphological profiles for large-scale drug discovery efforts. Viral pseudotime models can, in principle, be developed for any viruses or biological processes through a defined life cycle, suggesting that phenotypic time points, or phases, can be mapped across data in a broad range of applications. We report a high-content pseudotime framework for mapping temporal phenotypes and apply it to drug repurposing for DENV antivirals, gaining insight into how the mode of action of the screened antivirals correlates to phases of DENV replication. This study demonstrates how image-derived phenotypes can be leveraged to generate mode of action hypotheses and prioritize antiviral candidates for follow-up studies.