Award details

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

Reference	BB/W020424/1
Principal Investigator / Supervisor	Dr Tania Dottorini
Co-Investigators / Co-Supervisors	Dr Henry Adenuga, Dr Niklaas Buurma
Institution	University of Nottingham
Department	School of Veterinary Medicine and Sci
Funding type	Research
Value (£)	201,620
Status	Current
Type	Research Grant
Start date	01/09/2022
End date	29/02/2024
Duration	18 months

Abstract

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

Summary

The fight against enteric infections while containing the uprise of antimicrobial resistance, represents one of the major challenges in contemporary broiler farming, with repercussions on both bird and consumer's health. Key to future, better solutions for surveillance, diagnostics and treatment selection, is to gain an improved understanding of the bird's gut microbiome, exploring the modifications its population of commensals and opportunistic pathogens undergo as a consequence of infection, treatment and development of resistant traits. In this project, we plan to explore the broiler gut microbiome, focusing on infection and resistance in relation to pathogens typically found in the gastrointestinal tract of the birds: Clostridium perfringens, Enterococcus cecorum, Escherichia coli and Salmonella spp. We cover also scenarios of co-infection with viruses causing dysbiosis of gut microbiome. We consider resistance/susceptibility to 8 classes of antibiotics: tetracyclines, sulphonamides, beta-lactams, fluoroquinolones, polymyxins, macrolides, diaminopyrimidines, aminoglycosides, whose use as therapeutics is diffused in the UK. We plan to collect a large amount of heterogeneous data from farms, feed and birds, covering normal production periods and infection events. Data will include results of microbiological analysis, whole-genome sequencing, shotgun metagenomics and phenotyping performed on faecal samples, on-farm management practices, as well as environmental sensor data and bird imaging. We propose to use machine learning and cloud computing to perform large-scale data mining and ultimately unravel the network of possible 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. Acquired knowledge may provide hints at the selection of observable variables acting as biomarkers, i.e, targetable by future solutions for real-time livestock monitoring, to detect/forecast infection or the presence/insurgence of resistant traits, and to support precision diagnostics and bespoke treatment selection. The results may also suggest routes to improve the birds gut microbiome, for example via feed additives, making it more robust to infection while at the same time inhibiting the development of resistance.

Committee	Not funded via Committee
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	Endemic Livestock Disease Systems [2022]
Funding Scheme	X – not Funded via a specific Funding Scheme

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