Monitoring the gut microbiome via AI and omics: a new approach to detect infection and AMR and to support novel therapeutics in broiler precision farm

The production of poultry for meat consumption (broilers) is rising globally, the UK being amongst the countries with the highest production. Poultry meat consumption pro capita in the UK is twice more than pork and almost three times more than beef, and growing. Poultry endemic diseases due to bacteria, viruses and parasites are frowned upon, as they can cause considerable economic losses. To save production, the use of broad-spectrum antibiotics at any sign of incipient disease is widespread, even when the source of the disease has not been pinpointed yet (let alone the bacterial origin). The act of administering antibiotics increases the risk of the pathogen developing resistance (antimicrobial resistance - AMR), making it more difficult to fight that pathogen in the future. To reduce the use of broad-spectrum antibiotics, solutions are urgently needed for farms to efficiently monitor livestock, identify infections and the source of infection as soon as possible, and administer more targeted therapeutics.

The project aims at developing new surveillance solutions specifically designed for use by the broiler industry. These solutions are designed to be turn-key: operators will periodically upload data acquired within the farm to a cloud-based service where the state of production will be assessed automatically. Warnings and advice will be sent back to the farmers via apps on smartphones/tablets, in case infections, co-infection or increased likelihood of AMR are detected.
The project will cover the main pathogens of bacterial, viral and parasitic origin affecting UK broiler farming, as well as AMR to the main classes of antibiotics routinely administered in the country.

How will surveillance solutions achieve their predictions, and how will we decide what data to upload? At the core of the project there is a data mining method powered by machine learning, recently perfected by the applicants. The method allows to consider a large amount of heterogeneous information collected from the farm, including historical data of previous infections/AMR events, and allows the development of mathematical models that, based on observing specific patterns in the collected information, estimate the likelihood of infection or resistance manifestations. The method also allows to isolate what farm variables are the most important for each type of prediction (e.g. a specific infection, or AMR trait): these variables are called "biomarkers". Initially, we will consider many variables: sensor data on temperature, humidity, illumination and air composition in the barn, microbiological analysis of samples from feathers, soil, barn floors, water, feed, and operator boots. An important role is reserved to data originating from the analysis of the gut microbiome, i.e. the microbial species living in the broiler gut, whose abundances, genetic traits and metabolic functions, have been proven implicated in numerous aspects of infection and resistance. Co-presence of viruses and parasites will be considered. Thanks to machine learning, for the first time it will be possible to prune such a multitude of variables, isolating the most relevant (biomarkers) to be used in the final prediction models. These models will be turned into software applications running remotely as cloud services. Users (farmers) will periodically upload information (biomarker values) as required, allowing for the models to replicate exactly at any time the state of the real production (models will become "digital twins", being virtual replicas of the real system). Farmers will then receive messages via web-based apps, reporting warnings, alarms, or suggested therapies.
The methods for identifying the important variables and developing prediction models have been successful in pilot studies, leading to the identification of promising biomarkers documented in publications. The projected impact of the project on surveillance in broiler farming is expected to be unprecedented.

Research on precision livestock farming has been increasingly recognising the importance of studying the gut microbiome, resistome and its metabolites, as an invaluable source of information in relation to animal health and welfare. The population of gut microbiota changes in complex ways, as a consequence of external factors (environment, feed, etc.), but also as a consequence of infection, co-infection, diseases, and therapeutics.

In recent work on diseases and antimicrobial resistance (AMR) in broiler farming, we demonstrated that valuable information can be extracted from the bird gut microbiome. Through the development of a custom data mining method based on machine learning (ML), we uncovered evidence of correlations between gut metagenome modifications (metagenome and composition of the microbial community), environmental variables (temperature and humidity) and the likelihood of finding antimicrobial resistance (AMR) in reference pathogens within the farm (E. coli). We discovered evidence of resistance traits shared by birds, environments, and produced meat, and isolated hot-spots where infections and resistances tend to concentrate the most within the farm.

In this project, we plan to improve our ML methods to include a much larger set of variables. Within the gut, the metabolome and the possible co-presence of viruses and parasites. Within the farm, illumination and air composition, and data from optical/IR imaging and acoustic sensing. Genomics and metagenomics on many additional types of biological samples (barn floors, operator boots, drinking water, dust, air, water reservoir) will be included.
Another novelty aspect will be the development of cloud-based surveillance systems. Technical innovations will be the adoption of cloud services to relieve farms from the burdens of computing and data storage, and the use of digital twins (from Industry 4.0) to support remote surveillance AI, with simple messaging sent back to the farmers.