Genetics and quantum chemistry as tools for unknown metabolite identification

Project Summary/Abstract: The SARS-CoV-2 virus and resulting COVID-19 pandemic has created the biggest global health crisis in ourlifetime. We have assembled a team of investigators with expertise in vaccine development, environmentalexposures, immunology, metabolomics, lipidomics, and modeling to discover metabolic predictive biomarkers(MPBs) of infection in ferrets. We will use ferrets, because they have already been shown to be an effectiveanimal model for human COVID-19 disease, and they are currently being used for vaccine development.Our study builds upon an NIH funded co-infection study in which ferrets will be infected with 4 different commonrespiratory viruses before infection by SARS-CoV-2. That study will determine the severity of infections andimmune responses, but it did not include metabolomics measurements. The hypothesis of the co-infections isthat the severity of SARS-CoV-2 infection will be attenuated with co-infection by another virus. We will be addinga group of ferrets that will be exposed to per- and polyfluoroalkyl substances (PFAS) prior to infection by SARS-CoV-2. PFAS have been shown to suppress the immune system in mice, and a limited number of studies havedemonstrated associations between severity of virus infection and levels of PFAS. PFAS bioaccumulate intissues and are common chemicals used in many everyday items such as plastic bottles and non-stick cookingpans, so this common environmental exposure could be an important variable in COVID-19 symptoms. Theferret model provides an ideal way to study the effect of PFAS on SARS-CoV-2 infection progression andoutcomes. For each group in the study (co-infection, PFAS, or control), 15 serum samples will be collected fromeach animal (n=6 for each group) over about 1 month, with SARS-CoV-2 infection occurring at the midpoint ofthe sampling. Thus, we will be able to derive detailed time-course measurements of metabolites and lipids andassociate these signals with phenotypic outcomes.We have 3 specific aims: 1) Conduct the co-infection and PFAS exposure studies in BSL-3 containment andcollect immunological and infectivity data. Serum samples will be collected and inactivated by a biosafety-approved protocol. 2) Measure metabolites and lipids using non-targeted LC-MS and NMR. NMR is faster andless expensive and will be used to prioritize samples for LC-MS. Background PFAS signals from animal housingequipment will be determined. 3) Model the metabolites and lipids with phenotypic outcomes. We will also modelthe influence of PFAS exposure on the lipidome to better understand the molecular mechanisms of PFASimmunotoxicity.We have also started a Slack workspace for communication between different groups around the worldworking on COVID-19 metabolomics. This workspace provides for sharing of protocols and data, posting thelatest research in this area, as well as a forum for questions and answers. All data generated from our study will be shared publicly as soon as it passes our system suitability tests.