[22-EEID] Ecology and evolution of pathogen-microbiome-host interactions during population-level intermingling

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Commingling events occur when unfamiliar animals or people come together in a defined space and time with intensive and sustained contact. Commingling is associated with increased infectious pathogen transmission risk with possible global consequences, as the COVID-19 pandemic has highlighted. Commingling events in humans include mass-gathering events, back-to-school, air travel, incarceration, and mass migration. In livestock production, commingling routinely occurs in beef finishing systems and may occur on a national level when farmers rebuild herds following depopulation events, such as the recent foot-and-mouth disease outbreaks in the U.K. Commingling induces multi-level ecosystem disruption including perturbed social structure, co-circulating viral variants, host immune and physiological dyscrasia, and unstable microbial dynamics. We hypothesize that these interrelated processes create opportunity for viral transmission via three distinct mechanisms: 1) exposure of hosts to previously unseen viral variants; 2) host physiologic stress, including increased inflammation and immune system perturbation; and 3) ecological and evolutionary shifts in the microbiome. To test these hypotheses, we will perform controlled commingling trials using cattle relative to bovine coronavirus (BCV) transmission as a model system and pathogen, respectively. Using unique calf populations and facilities in the US and UK, we will generate highly-resolved time series datasets for BCV variant behaviour, host immune-inflammatory responses, and microbiome dynamics during commingling. Microbiome and BCV data will be analysed at the nucleotide level to uncover temporal variant- and strain-level ecology and evolution during commingling. We will model host immune-inflammatory responses as a multi-component system using specific markers and transcriptome analysis. We will then use these data to populate two novel temporal models of virus behaviour: first, an epidemiological risk factor model for disease during commingling using dynamic Bayesian network analysis; and second, a spatiotemporal SEIR pathogen transmission model that incorporates parameters for host immune response and microbiome eco-evolutionary shifts during commingling events.Intellectual MeritThe world is experiencing an inexorable trend towards increasingly frequent, intensive, large-scale commingling events among humans and animals. This has ramifications for viral transmission and variant evolution. There is an urgent need to understand the theoretical basis of virus dynamics specifically during these commingling events. A largely unexplored component of viral transmission during commingling is the host microbiome, which experiences dramatic shifts during commingling events. Our work has intellectual merit because it explicitly models these microbiome dynamics within a transmission and risk factor modelling framework. This will allow us to uncover organizing principles of viral transmission during commingling, which will advance theoretical understanding of virus behaviour at the variant level. Our work also improves and extends existing infectious disease modelling approaches by incorporating critical commingling and microbiome dynamics with temporal and host stochasticity. Finally, we would provide the scientific community with a highly-resolved empirical dataset as well as a novel study platform for future research on infectious disease dynamics during commingling events.Broader ImpactsWe propose to develop an integrated program that will train veterinary students in research experiences and advanced epidemiological methods that will help build the next generation of agricultural leaders and researchers. We also propose a series of Bioinformatics Workshops to engage agricultural researchers in primary analysis of genomic data. Our project outcomes will have immediate relevance to livestock husbandry practices.