Host cell cycle checkpoint as antiviral target for SARS-CoV-2 revealed by integrative transcriptome and proteome analyses

Dynamic label-free analysis of SARS-CoV-2 infection reveals virus-induced subcellular remodeling

Assessing the impact of SARS-CoV-2 on organelle dynamics allows a better understanding of the mechanisms of viral replication. We combine label-free holotomographic microscopy with Artificial Intelligence to visualize and quantify the subcellular changes triggered by SARS-CoV-2 infection. We study the dynamics of shape, position and dry mass of nucleoli, nuclei, lipid droplets and mitochondria within hundreds of single cells from early infection to syncytia formation and death. SARS-CoV-2 infection enlarges nucleoli, perturbs lipid droplets, changes mitochondrial shape and dry mass, and separates lipid droplets from mitochondria. We then used Bayesian network modeling on organelle dry mass states to define organelle cross-regulation networks and report modifications of organelle cross-regulation that are triggered by infection and syncytia formation. Our work highlights the subcellular remodeling induced by SARS-CoV-2 infection and provides an Artificial Intelligence-enhanced, label-free methodology to study in real-time the dynamics of cell populations and their content.

USP2 inhibition prevents infection with ACE2-dependent coronaviruses in vitro and is protective against SARS-CoV-2 in mice

Targeting angiotensin-converting enzyme 2 (ACE2) represents a promising and effective approach to combat not only the COVID-19 pandemic but also potential future pandemics arising from coronaviruses that depend on ACE2 for infection. Here, we report ubiquitin specific peptidase 2 (USP2) as a host-directed antiviral target; we further describe the development of MS102, an orally available USP2 inhibitor with viable antiviral activity against ACE2-dependent coronaviruses. Mechanistically, USP2 serves as a physiological deubiquitinase of ACE2, and targeted inhibition with specific small-molecule inhibitor ML364 leads to a marked and reversible reduction in ACE2 protein abundance, thereby blocking various ACE2-dependent coronaviruses tested. Using human ACE2 transgenic mouse models, we further demonstrate that ML364 efficiently controls disease caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as evidenced by reduced viral loads and ameliorated lung inflammation. Furthermore, we improved the in vivo performance of ML364 in terms of both pharmacokinetics and antiviral activity. The resulting lead compound, MS102, holds promise as an oral therapeutic option for treating infections with coronaviruses that are reliant on ACE2.

Evaluating anti-viral effect of Ivermectin on porcine epidemic diarrhea virus and analyzing the related genes and signaling pathway by RNA-seq in vitro

Porcine epidemic diarrhea virus (PEDV) has catastrophic impacts on the global pig industry. However, there remains no effective drugs for PEDV infection. Ivermectin is an FDA-approved anthelmintic drug used to treat worm infections. In this study, we reported the broad-spectrum antiviral activity of Ivermectin in vitro. Ivermectin can inhibit PEDV infections of different genotypes. Avermectin derivatives can also inhibit PEDV infections. A time of addition assay showed that Ivermectin exhibited potent anti-PEDV activity when added simultaneously with or post virus infection. Furthermore, Ivermectin significantly inhibited the late stage of viral infection by affecting viral release. RNA sequencing indicates Ivermectin induces cell cycle arrest, which may be related to its ability to inhibit viral release. Interestingly, when combined with Niclosamide, Ivermectin demonstrated an enhanced anti-PEDV effect. These findings highlight Ivermectin as a novel antiviral agent with potential for the development of drugs against PEDV infection.

Transcriptome from Paired Samples Improves the Power of Comprehensive COVID-19 Host-Viral Characterization

Previous transcriptome profiling studies showed significantly upregulated genes and altered biological pathways in acute COVID-19. However, changes in the transcriptional signatures during a defined time frame are not yet examined and described. The aims of this study included viral metagenomics and evaluation of the total expression in time-matched and tissue-matched paired COVID-19 samples with the analysis of the host splicing profile to reveal potential therapeutic targets. Prospective analysis of paired nasopharyngeal swabs (NPS) and blood (BL) samples from 18 COVID-19 patients with acute and resolved infection performed using Kallisto, Suppa2, Centrifuge, EdgeR, PantherDB, and L1000CDS2 tools. In NPS, we discovered 6 genes with changed splicing and 40 differentially expressed genes (DEG) that yielded 88 altered pathways. Blood samples yielded 15 alternatively spliced genes. Although the unpaired DEG analysis failed, pairing identified 78 genes and 242 altered pathways with meaningful clinical interpretation and new candidate drug combinations with up to 65% overlap. Metagenomics analyses showed SARS-CoV-2 dominance during and after the acute infection, with a significant reduction in NPS (0.008 vs. 0.002, p = 0.019). Even though both NPS and BL give meaningful insights into expression changes, this is the first demonstration of how the power of blood analysis is vastly maximized by pairing. The obtained results essentially showed that pairing is a determinant between a failed and a comprehensive study. Finally, the bioinformatics results prove to be a comprehensive tool for full-action insights, drug development, and infectious disease research when designed properly.

Machine learning on large scale perturbation screens for SARS-CoV-2 host factors identifies β-catenin/CBP inhibitor PRI-724 as a potent antiviral

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.

Roles of p53-Mediated Host–Virus Interaction in Coronavirus Infection

The emergence of the SARS-CoV-2 coronavirus has garnered global attention due to its highly pathogenic nature and the resulting health crisis and economic burden. Although drugs such as Remdesivir have been considered a potential cure by targeting the virus on its RNA polymerase, the high mutation rate and unique 3’ to 5’ exonuclease with proofreading function make it challenging to develop effective anti-coronavirus drugs. As a result, there is an increasing focus on host–virus interactions because coronaviruses trigger stress responses, cell cycle changes, apoptosis, autophagy, and the dysregulation of immune function and inflammation in host cells. The p53 tumor suppressor molecule is a critical regulator of cell signaling pathways, cellular stress responses, DNA repair, and apoptosis. However, viruses can activate or inhibit p53 during viral infections to enhance viral replication and spread. Given its pivotal role in cell physiology, p53 represents a potential target for anti-coronavirus drugs. This review aims to summarize the relationship between p53 and coronaviruses from various perspectives, to shed light on potential targets for antiviral drug development and vaccine design.