# Genetics and quantum chemistry as tools for unknown metabolite identification > **NIH NIH U2C** · UNIVERSITY OF GEORGIA · 2020 · $351,409 ## Abstract Project Summary/Abstract The SARS-CoV-2 virus and resulting COVID-19 pandemic has created the biggest global health crisis in our lifetime. We have assembled a team of investigators with expertise in vaccine development, environmental exposures, 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 effective animal 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 common respiratory viruses before infection by SARS-CoV-2. That study will determine the severity of infections and immune responses, but it did not include metabolomics measurements. The hypothesis of the co-infections is that the severity of SARS-CoV-2 infection will be attenuated with co-infection by another virus. We will be adding a 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 have demonstrated associations between severity of virus infection and levels of PFAS. PFAS bioaccumulate in tissues and are common chemicals used in many everyday items such as plastic bottles and non-stick cooking pans, so this common environmental exposure could be an important variable in COVID-19 symptoms. The ferret model provides an ideal way to study the effect of PFAS on SARS-CoV-2 infection progression and outcomes. For each group in the study (co-infection, PFAS, or control), 15 serum samples will be collected from each animal (n=6 for each group) over about 1 month, with SARS-CoV-2 infection occurring at the midpoint of the sampling. Thus, we will be able to derive detailed time-course measurements of metabolites and lipids and associate these signals with phenotypic outcomes. We have 3 specific aims: 1) Conduct the co-infection and PFAS exposure studies in BSL-3 containment and collect 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 and less expensive and will be used to prioritize samples for LC-MS. Background PFAS signals from animal housing equipment will be determined. 3) Model the metabolites and lipids with phenotypic outcomes. We will also model the influence of PFAS exposure on the lipidome to better understand the molecular mechanisms of PFAS immunotoxicity. We have also started a Slack workspace for communication between different groups around the world working on COVID-19 metabolomics. This workspace provides for sharing of protocols and data, posting the latest research in this area, as well as a forum for questions and answers. All data generated from our study will be shared publicly ... ## Key facts - **NIH application ID:** 10173229 - **Project number:** 3U2CES030167-03S2 - **Recipient organization:** UNIVERSITY OF GEORGIA - **Principal Investigator:** ARTHUR S EDISON - **Activity code:** U2C (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $351,409 - **Award type:** 3 - **Project period:** 2018-09-01 → 2022-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10173229 ## Citation > US National Institutes of Health, RePORTER application 10173229, Genetics and quantum chemistry as tools for unknown metabolite identification (3U2CES030167-03S2). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10173229. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*