Viruses utilize host cell signaling to facilitate productive infection. Equine herpesvirus type 1 (EHV-1) has been shown to activate Ca2+ release and phospholipase C upon contact with α4β1 integrins on the cell surface. Signaling molecules, including small GTPases, have been shown to be activated downstream of Ca2+ release, and modulate virus entry, membrane remodeling and intracellular transport. In this study, we show that EHV-1 activates the small GTPases Rac1 and Cdc42 during infection. The activation of Rac1 and Cdc42 is necessary for virus-induced acetylation of tubulin, effective viral transport to the nucleus, and cell-to-cell spread. We also show that inhibitors of Rac1 and Cdc42 did not block virus entry, but inhibited overall virus infection. The Rac1 and Cdc42 signaling is presumably orthogonal to Ca2+ release, since Rac1 and Cdc42 inhibitors affected the infection of both EHV-1 and EHV-4, which do not bind to integrins.

Some of the emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are less susceptible to neutralization with post-vaccine sera and monoclonal antibodies targeting the viral spike glycoprotein. This raises concerns of disease control, transmissibility, and severity. Numerous substitutions have been identified to increase viral fitness within the nucleocapsid and nonstructural proteins, in addition to spike mutations. Therefore, we sought to generate infectious viruses carrying only the variant-specific spike mutations in an identical backbone to evaluate the impact of spike and non-spike mutations in the virus life cycle. We used en passant mutagenesis to generate recombinant viruses carrying spike mutations of B.1 and B.1.617.2 variants using SARS-CoV-2- bacterial artificial chromosome (BAC). Neutralization assays using clinical sera yielded comparable results between recombinant viruses and corresponding clinical isolates. Non-spike mutations for both variants neither seemed to effect neutralization efficiencies with monoclonal antibodies nor the response to treatment with inhibitors. However, live-cell imaging and microscopy revealed differences, such as persisting syncytia and pronounced cytopathic effect formation, as well as their progression between BAC-derived viruses and clinical isolates in human lung epithelial cell lines and primary bronchial epithelial cells. Complementary RNA analyses further suggested a potential role of non-spike mutations in infection kinetics.

Expanding antiviral treatment options against SARS-CoV-2 remains crucial as the virus evolves rapidly and drug resistant strains have emerged. Broad spectrum host-directed antivirals (HDA) are promising therapeutic options, however the robust identification of relevant host factors by CRISPR/Cas9 or RNA interference screens remains challenging due to low consistency in the resulting hits. To address this issue, we employed machine learning based on experimental data from knockout screens and a drug screen. As gold standard, we assembled perturbed genes reducing virus replication or protecting the host cells. The machines based their predictions on features describing cellular localization, protein domains, annotated gene sets from Gene Ontology, gene and protein sequences, and experimental data from proteomics, phospho-proteomics, protein interaction and transcriptomic profiles of SARS-CoV-2 infected cells. The models reached a remarkable performance with a balanced accuracy of 0.82 (knockout based classifier) and 0.71 (drugs screen based classifier), suggesting patterns of intrinsic data consistency. The predicted host dependency factors were enriched in sets of genes particularly coding for development, morphogenesis, and neural related processes. Focusing on development and morphogenesis-associated gene sets, we found β-catenin to be central and selected PRI-724, a canonical β-catenin/CBP disruptor, as a potential HDA. PRI-724 limited infection with SARS-CoV-2 variants, SARS-CoV-1, MERS-CoV and IAV in different cell line models. We detected a concentration-dependent reduction in CPE development, viral RNA replication, and infectious virus production in SARS-CoV-2 and SARS-CoV-1-infected cells. Independent of virus infection, PRI-724 treatment caused cell cycle deregulation which substantiates its potential as a broad spectrum antiviral. Our proposed machine learning concept may support focusing and accelerating the discovery of host dependency factors and the design of antiviral therapies.

Expanding antiviral treatment options against SARS-CoV-2 remains crucial as the virus evolves under selection pressure which already led to the emergence of several drug resistant strains. Broad spectrum host-directed antivirals (HDA) are promising therapeutic options, however the robust identification of relevant host factors by CRISPR/Cas9 or RNA interference screens remains challenging due to low consistency in the resulting hits. To address this issue, we employed machine learning, based on experimental data from several knockout screens and a drug screen. We trained classifiers using genes essential for virus life cycle obtained from the knockout screens. The machines based their predictions on features describing cellular localization, protein domains, annotated gene sets from Gene Ontology, gene and protein sequences, and experimental data from proteomics, phospho-proteomics, protein interaction and transcriptomic profiles of SARS-CoV-2 infected cells. The models reached a remarkable performance suggesting patterns of intrinsic data consistency. The predicted HDF were enriched in sets of genes particularly encoding development, morphogenesis, and neural processes. Focusing on development and morphogenesis-associated gene sets, we found β-catenin to be central and selected PRI-724, a canonical β-catenin/CBP disruptor, as a potential HDA. PRI-724 limited infection with SARS-CoV-2 variants, SARS-CoV-1, MERS-CoV and IAV in different cell line models. We detected a concentration-dependent reduction in cytopathic effects, viral RNA replication, and infectious virus production in SARS-CoV-2 and SARS-CoV-1-infected cells. Independent of virus infection, PRI-724 treatment caused cell cycle deregulation which substantiates its potential as a broad spectrum antiviral. Our proposed machine learning concept supports focusing and accelerating the discovery of host dependency factors and identification of potential host-directed antivirals.