Biology subjects

Kawaoka, Y.

Publications and source records attributed to Kawaoka, Y..

39 records · Page 3Linked to original sources

bioRxiv2020

The anticoagulant nafamostat potently inhibits SARS-CoV-2 infection in vitro: an existing drug with multiple possible therapeutic effects

Although infection by SARS-CoV-2, the causative agent of COVID-19, is spreading rapidly worldwide, no drug has been shown to be sufficiently effective for treating COVID-19. We previously found that nafamostat mesylate, an existing drug used for disseminated intravascular coagulation (DIC), effectively blocked MERS-CoV S protein-initiated cell fusion by targeting TMPRSS2, and inhibited MERS-CoV infection of human lung epithelium-derived Calu-3 cells. Here we established a quantitative fusion assay dependent on SARS-CoV-2 S protein, ACE2 and TMPRSS2, and found that nafamostat mesylate potently inhibited the fusion while camostat mesylate was about 10-fold less active. Furthermore, nafamostat mesylate blocked SARS-CoV-2 infection of Calu-3 cells with an EC50 around 10 nM, which is below its average blood concentration after intravenous administration through continuous infusion. These findings, together with accumulated clinical data regarding its safety, make nafamostat a likely candidate drug to treat COVID-19.

microbiology↗

bioRxiv2020

Limited SARS-CoV-2 diversity within hosts and following passage in cell culture

Since the first reports of pneumonia associated with a novel coronavirus (COVID-19) emerged in Wuhan, Hubei province, China, there have been considerable efforts to sequence the causative virus, SARS-CoV-2 (also referred to as hCoV-19) and to make viral genomic information available quickly on shared repositories. As of 30 March 2020, 7,680 consensus sequences have been shared on GISAID, the principal repository for SARS-CoV-2 genetic information. These sequences are primarily consensus sequences from clinical and passaged samples, but few reports have looked at diversity of virus populations within individual hosts or cultures. Understanding such diversity is essential to understanding viral evolutionary dynamics. Here, we characterize within-host viral diversity from a primary isolate and passaged samples, all originally deriving from an individual returning from Wuhan, China, who was diagnosed with COVID-19 and subsequently sampled in Wisconsin, United States. We use a metagenomic approach with Oxford Nanopore Technologies (ONT) GridION in combination with Illumina MiSeq to capture minor within-host frequency variants [≥]1%. In a clinical swab obtained from the day of hospital presentation, we identify 15 single nucleotide variants (SNVs) [≥]1% frequency, primarily located in the largest gene - ORF1a. While viral diversity is low overall, the dominant genetic signatures are likely secondary to population size changes, with some evidence for mild purifying selection throughout the genome. We see little to no evidence for positive selection or ongoing adaptation of SARS-CoV-2 within cell culture or in the primary isolate evaluated in this study. Author SummaryWithin-host variants are critical for addressing molecular evolution questions, identifying selective pressures imposed by vaccine-induced immunity and antiviral therapeutics, and characterizing interhost dynamics, including the stringency and character of transmission bottlenecks. Here, we sequenced SARS-CoV-2 viruses isolated from a human host and from cell culture on three distinct Vero cell lines using Illumina and ONT technologies. We show that SARS-CoV-2 consensus sequences can remain stable through at least two serial passages on Vero 76 cells, suggesting SARS-CoV-2 can be propagated in cell culture in preparation for in-vitro and in-vivo studies without dramatic alterations of its genotype. However, we emphasize the need to deep-sequence viral stocks prior to use in experiments to characterize sub-consensus diversity that may alter outcomes.

microbiology↗

bioRxiv2018

Network-guided Discovery of Influenza Virus Replication Host Factors

The position of host factors required for viral replication within a human protein-protein interaction (PPI) network can be exploited to identify drug targets that are robust to drug-mediated selective pressure. Host factors can physically interact with viral proteins, be a component of pathways regulated by viruses (where proteins themselves do not interact with viral proteins) or be required for viral replication but unregulated by viruses. Here, we demonstrate a method of combining a human PPI network with virus-host protein interaction data to improve antiviral drug discovery for influenza viruses by identifying target host proteins. Network analysis shows that influenza virus proteins physically interact with host proteins in network positions significant for information flow. We have isolated a subnetwork of the human PPI network which connects virus-interacting host proteins to host factors that are important for influenza virus replication without physically interacting with viral proteins. The subnetwork is enriched for signaling and immune processes. Selecting proteins based on network topology within the subnetwork, we performed an siRNA screen to determine if the subnetwork was enriched for virus replication host factors and if network position within the subnetwork offers an advantage in prioritization of drug targets to control influenza virus replication. We found that the subnetwork is highly enriched for target host proteins - more so than the set of host factors that physically interact with viral proteins. Our findings demonstrate that network positions are a powerful predictor to guide antiviral drug candidate prioritization.\n\nIMPORTANCEIntegrating virus-host interactions with host protein-protein interactions, we have created a method using these established network practices to identify host factors (i.e. proteins) that are likely candidates for antiviral drug targeting. We demonstrate that interaction cascades between host proteins that directly interact with viral proteins and host factors that are important to influenza replication are enriched for signaling and immune processes. Additionally, we show that host proteins that interact with viral proteins are in network locations of power. Finally, we demonstrate a new network methodology to predict novel host factors and validate predictions with an siRNA screen. Our results show that integrating virus-host proteins interactions is useful in the identification of antiviral drug target candidates.

systems biology↗