Development of a pseudo-typed virus particle based method to determine the efficacy of virucidal agents.

The ongoing Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic has highlighted the threat that viral outbreaks pose to global health. A key tool in the arsenal to prevent and control viral disease outbreaks is disinfection of equipment and surfaces with formulations that contain virucidal agents (VA). However, assessment of the efficacy of virus inactivation often requires live virus assays or surrogate viruses such as Modified Vaccinia Virus Ankara (MVA), which can be expensive, time consuming and technically challenging. Therefore, we have developed a pseudo-typed virus (PV) based approach to assess the inactivation of enveloped viruses with a fast and quantitative output that can be adapted to emerging viruses. Additionally, we have developed a method to completely remove the cytotoxicity of virucidal agents while retaining the required sensitivity to measure PV infectivity. Our results indicated that the removal of cytotoxicity was an essential step to accurately measure virus inactivation. Further, we demonstrated that there was no difference in susceptibility to virus inactivation between PVs that express the envelopes of HIV-1, SARS-CoV-2, and Influenza A/Indonesia. Therefore, we have developed an effective and safe alternative to live virus assays that enables the rapid assessment of virucidal activity for the development and optimization of virucidal reagents.

An efficient and scalable top-down method for predicting structures of microbial communities.

Modern applications involving multispecies microbial communities rely on the ability to predict structures of such communities in defined environments. The structures depend on pairwise and high-order interactions between species. To unravel these interactions, classical bottom-up approaches examine all possible species subcommunities. Such approaches are not scalable as the number of subcommunities grows exponentially with the number of species, n. Here we present a top-down method wherein the number of subcommunities to be examined grows linearly with n, drastically reducing experimental effort. The method uses steady-state data from leave-one-out subcommunities and mathematical modeling to infer effective pairwise interactions and predict community structures. The accuracy of the method increases with n, making it suitable for large communities. We established the method in silico and validated it against a five-species community from literature and an eight-species community cultured in vitro. Our method offers an efficient and scalable tool for predicting microbial community structures.