Fighting Infection and AMR in broiler farming: AI omics and smart sensing for diagnostics treatment selection and gut microbiome improvement

The gut microbiome is composed of harmless symbionts, commensal bacteria, and opportunistic pathogens, all of which play crucial roles in animal health and disease. In physiological conditions the gut microbiome is stable, but when perturbative events occur (e.g., dietary changes, infections, stress, antibiotic administration) the population of microbiota changes, influencing health and protection against infections and colonisation. These changes may involve new resistant bacteria becoming permanent residents, or transferring resistance to the commensals. In poultry farming, all these mechanisms are still largely unknown, but the importance of studying the gut microbiome in connection to farming productivity has been acknowledged, recognising also the existence of numerous environmental and practice-related factors influencing gut modifications. The aim of this project is to introduce novel approaches to precision farming, based on a better understanding of infection and resistance of specific pathogens (Clostridium perfringens, Enterococcus cecorum, Escherichia coli and Salmonella) and relationships with the gut microbiome. We will collect a large amount of heterogeneous data covering a broad range of targets (birds, soil, feed, water, air), involving a broad range of sources (sensing, imaging, microbiological analysis, whole-genome sequencing, shotgun metagenomics, on-farm management practices), and covering multiple time points and conditions. We will use machine learning and cloud computing to perform large-scale data mining and ultimately unravel the network of interactions amongst the observable variables, following broilers along their life cycle, and capturing episodes of infection, treatment and development of single or multi-drug resistance. The acquired knowledge will be used to select a viable set of monitorable variables to implement real-time forecasting and diagnostics of infection and AMR, and to devise decision support tools for treatment selection