Marek's disease (MD) causes paralysis and tumours in chickens. It is caused by serotype 1 strains of the Marek's disease virus (MDV-1) which is shed from skin of infected chickens and persists for many months in dust in contaminated poultry houses. It is highly contagious and spreads to other chickens by inhalation. MD is a major disease affecting poultry health, welfare, and productivity, with annual estimated loss to the global poultry industry of $2 billion. MD is endemic in UK poultry but is effectively controlled by live vaccine viruses, which are harmless relatives of MDV-1, and include CVI988, HVT, and MDV serotype 2 (MDV-2). MDV-2 vaccines are widely used in the Americas and Asia but not in the UK. However, by testing samples collected from poultry farms, we found MDV-2 is widespread in the UK. MDV-2 strains circulate freely and naturally at high levels and persist long-term in the flock, but little is known about them; are they derived from vaccine strains which 'escaped' from imported poultry, or are they naturally occurring strains? Chickens can be infected with any combination of MDV-1, MDV-2, and vaccine viruses at the same time (co-infection). We have found MDV-2 in healthy chicken flocks, as well as flocks that have MD. We would like to know whether co-infection with MDV-2 affects flock health and disease, and production parameters such as egg production and mortality, and whether certain MDV-2 strains could be used as effective recombinant vaccines against MD and other poultry diseases. Our objectives are to: (1) Investigate prevalence of naturally occurring MDV-2 infection in the field, and it's influence on flock productivity, immune responses and disease, (2) Characterise MDV-2 field isolates and (3) Exploit novel MDV-2 as potential viral vectors for novel recombinant vaccines. The project is a partnership with poultry industry vets. We will select two MDV-2-positive and two MDV-2-negative flocks for two bird types (broiler-breeder, layer) for regular sampling to collect blood samples from chickens and dust from the housing sheds. We will also collect data on flock health and productivity. At Pirbright, we will test the samples by 'polymerase chain reaction' to detect the genetic material of MDV-1, MDV-2 and vaccine viruses to show the kinetics of MDV-2 infection and shedding, and the frequency of co-infection with MDV-1 field strains and vaccine viruses. Using mathematical modelling, we will also investigate dynamics of transmission of MDV-2 within flocks. We will determine variability of MDV-2 strains by sequencing the virus genetic material and comparing with known MDV-2 strains. We will study the characteristics of selected MDV-2 strains by growing these viruses in cell culture then using them to infect chickens under controlled laboratory conditions to examine replication, persistence, clinical signs and transmission of MDV-2. Most MDV-2 strains have characteristics which make them suitable as vaccines against MD: they do not cause disease, they grow well in the chicken and persist for many months, and they are easily transmitted between chickens to maintain a high level of exposure of the flock to vaccine virus. Furthermore, MDV-2 can be genetically engineered to carry genes from other important poultry viral pathogens, e.g., infectious bursal disease virus (IBDV) and Newcastle disease virus (NDV); a recombinant 'vectored vaccine' like this could potentially protect chickens against IBD and ND as well as MD in a single vaccination. We will engineer an appropriate MDV-2 strain to create a 'rMDV2-IBD-ND' virus, then test its ability to protect chickens against these three diseases under controlled laboratory conditions. This study is important to understand the effect of widespread MDV-2 infection on health and productivity of commercial poultry flocks. A new recombinant MDV-2 vaccine would be a useful addition to the set of live virus vaccines used to control MD and other poultry diseases.

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.

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.