Fighting Infection and AMR in broiler farming.

The fight against enteric infections and antimicrobial resistance represents a major challenge in contemporary broiler farming. Infections caused by Clostridium perfringens, Enterococcus cecorum, Escherichia coli and Salmonella spp. are a significant cause of morbidity, mortality, poor welfare and economical losses in broiler farming. The gut microbiome is composed of harmless symbionts, commensal bacteria, and opportunistic pathogens, all of which play crucial roles in 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 further colonisation.
Key to better solutions for surveillance, diagnostics and treatment selection, is exploring the modifications gut microorganisms undergo as a consequence of infection, treatment and development of resistant traits.
With the proliferation of collectable information, research has been gradually moving towards the adoption of the latest technologies in machine learning (ML) and big data mining to implement precision poultry farming. In this project building on methods previously developed by Dr Dottorini, we will 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. The project will utilise a large amount of heterogeneous data from farms, feed and birds, including sequencing, microbiological and sensor data, collected as part of a BBSRC-funded project 6784258.
The aim of this project is to introduce novel ML approaches to precision farming, based on a better understanding of infection and resistance and relationships with the gut microbiome. This will be achieved through three objectives: uncovering the broiler gut microbiome, exposing external correlations, and identifying biomarkers of infection events or resistance development. There will also be the opportunity to experimentally validate any promising biomarker candidates uncovered in the lab of Prof Paul Williams.
WP1: Use infection statuses and resistance profiles as detected from processing of biological samples to tag all the other data collected contextually from environment, birds, water and feed.
WP2: Use the previously illustrated methods and develop new pipelines to uncover correlations with gut microbiome modifications observed via biological sampling. Including the analysis of commensals and opportunistic pathogens, and the modifications of the resident and transient resistomes.
WP3: Extend the correlation analysis to identify relationships between gut microbiome modifications and changes in feed, water, environmental variables, WP4: Given the knowledge acquired on the correlations between infection, resistance, gut microbiome and other measurable variables (related to birds, feed, water, soil, air, etc.) identify subsets of variables which may act as predictors (i.e., biomarkers) of infection or resistance development. Selection driven by tradeoff between correlation strength and viability of application (technical and economical) within the farm.
For further background see papers:
- Peng et al. 2022. 'Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming', PLoS Computational Biology, 18: e1010018.
- Maciel-Guerra et al 2022. 'Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock', The ISME Journal