Genome-based source attribution using a One Health Escherichia coli isolate collection from 2013 to 2023 in Scotland

ABSTRACT Random Forest based source attribution models were developed from a ‘one health’ resource comprising 4,230 high-quality whole genome assemblies from E. coli . These were isolated from a wide range of sources, predominantly originating in Scotland, including wastewater, livestock, food, and clinical infections of humans and dogs. Using these models, we derived a probabilistic assignment of E. coli isolates from food, shellfish and water samples to potential livestock and human sources of contamination. The incorporation of E. coli sequences from wastewater alongside those from human clinical infections, enabled us to capture a wide diversity of human strains in our analyses. The sequence types (STs) of isolates from human bacteraemia and urinary tract infections (UTI) were compared with livestock and food isolates. While only 2.3% of the E. coli isolated from food samples in the study were from STs primarily associated with human bacteraemia and UTI, the models found a livestock signal associated with 15% of the human clinical isolates. In the food and private water samples, livestock-human co-attribution of E. coli isolates was common and consistent with routine human exposure to specific subsets of livestock E. coli , potentially a result of selection during food and water processing. Overall, this research demonstrates the potential value of including source attribution models in national surveillance programmes to understand the transmission of E. coli through the agri-food chain and support risk management to protect public health. IMPACT STATEMENT This research examines the genetic composition of Escherichia coli isolated from many different sources including, animals, humans, food, water and wastewater around Scotland. With the public resource generated, machine-learning models were developed to allow the source of an E. coli , for example one isolated from food or water, to be predicted from its genome sequence. We show that E. coli has genetic content associated with the originating host that allows source tracking using the developed models. Specifically, E. coli associated with human UTI and bloodstream infections are acquired predominately form human sources, although 15% of isolates exhibit livestock signals, especially pigs. Food, shellfish and water E. coli samples show low association with human clinical strains (∼5%) but over a third of food isolates co-associate to both livestock and human sources supporting food as key pathway to human colonisation. The sequence and associated data provided is a valuable One Health resource the models generated can identify the animal or human source of an E. coli isolate. This can help with outbreak tracing and defining the sources of food or water contamination to develop appropriate interventions.