Interrogating immune response and coronavirus infection in a migratory bat model

PROJECT SUMMARY Bats harbor many zoonotic viruses, including both genera of coronaviruses (CoVs) pathogenic in humans (α- and β-CoVs). Limited evidence to-date suggests that periods of active infection in bats-and thus opportunities to transmit zoonotic viruses to humans-are driven by stressors that modulate immunological tolerance of infection and facilitate viral replication and shedding. Yet such work has mostly ignored immune mechanisms and remains observational. This project will combine experimental field studies with bioinformatic analyses and mathematical modeling to test migration as a stressor in bats and its impact on CoV infection and shedding. We will focus on Mexican free-tailed bats (Tadarida brasiliensis), a common and widespread migratory bat species in North America for which we and others have detected CoVs similar to HCoV-229E and that is susceptible to SARS-CoV-2. In Aim 1, we will sample T. brasiliensis monthly at our established study site in Oklahoma, capturing energetic stressors of spring migration from Mexico, birth and lactation, and fall migration back to Mexico. In vivo challenge with polyIC, mimicking a natural RNA virus infection, will be used to query the bat antiviral response in the face of these different stressors. Established transcriptomic and proteomic assays will be performed at the OCIE ImmunoModulation Technology Core (IMTC) to quantify shifts in immune gene expression and protein abundance, characterizing both global response as well as the response of select antiviral components such as interferons. In Aim 2, we will characterize CoV infection from oral and rectal swabs using RT-PCR. Working closely with the OCIE Omics Data Science Core (ODSC), we will assess differential abundance of genes and proteins with CoV infection. Further, we will identify candidate gene and protein biomarkers of CoV infection using simple classifiers and robust machine learning algorithms. In Aim 3, we will adapt our previously established susceptible-infected-latent-infected mathematical model to our migratory bat system, integrating key immunological findings from Aims 1 and 2. We will further capitalize on our Motus Wildlife Tracking System infrastructure in Oklahoma and monitor bat migration using lightweight, suture-attached PowerTags. We will use resulting data on bat migration routes to run stochastic simulations of our model, generating predictions about when and where we expect bat CoV prevalence to be greatest. This project will establish an experimental and computational pipeline for studying how stressors affect bat-borne zoonoses and provide a key baseline for future efforts to mitigate virus spillover risks from migratory bats.