Award details

Unravelling Enterococcus cecorum infection on UK broiler farms: correlating clinical signs with genomics, persistence and animal behaviour.

Reference	BB/W020491/1
Principal Investigator / Supervisor	Professor Muna Anjum
Co-Investigators / Co-Supervisors	Professor Ilias Kyriazakis, Dr Katharina Watson
Institution	Animal and Plant Health Agency (APHA)
Department	Food and Environmental Safety
Funding type	Research
Value (£)	200,860
Status	Current
Type	Research Grant
Start date	17/05/2022
End date	17/07/2023
Duration	14 months

Abstract

In this exciting proposal the research team will tackle questions on how Enterococcus cecorum evolved from a commensal to an emergent pathogen, by performing whole genome sequencing (WGS). Bioinformatic pipelines, such as the APHA SeqFinder pipeline will help map raw reads from Illumina WGS to genes present in an updated APHA SeqFinder virulence/AMR genes database, to identify any variants present in isolates from diseased birds. Comparative genomics and circularisation of E. cecorum genomes from hybrid assemblies of short- and long-read WGS will identify virulence factors and antimicrobial resistance (AMR) genes gained been by horizontal transfer of mobile genetic elements such as plasmids, phages, transposons etc. uniquely present in isolates from diseased birds. Phylogenetic analysis will detect any lineages from diseased poultry enriched with virulence genes. These determinants will be incorporated into quantitative real-time (q)PCR assays and be the basis of rapid diagnostic pen-side tests in future. In-depth sampling will be performed by swabbing the environment on five farms that are identified to have a history of E. cecorum infection at different times in the poultry production cycle. A qPCR previously published for sensitive and specific detection of E. cecorum will be used to detect its presence in the environment. Stress studies performed on Enterococcus sp. will be adapted to determine if E. cecorum pathogens survive in hostile environments, and whether a particular variant/lineage is more successful than others. Video cameras and sensors associated with analytical behaviour software such as EyeNamic and Noldus EthoVision, that uses machine learning algorithms to translate video images into indices of behaviour, will be used for monitoring poultry houses. Quantification of behaviours over different time periods will help detect any subtle changes due to E. cecorum infection that can be verified by veterinary examinations.

Summary

Endemic disease such as lameness that may lead to paralysis and death in broiler chickens presents considerable welfare problems, it leads to significant antimicrobial usage and results in substantive economic losses for the broiler industry both within the United Kingdom and worldwide. Enterococcus cecorum, an emerging pathogen, has become associated with infections in affected poultry flocks in the British Broiler Industry. However, little is known about how this commensal has evolved to become a pathogen due to E. cecorum genomics being in its infancy. The environmental reservoir(s) that it occupies which results in apparently sporadic disease occurrence within poultry flocks is also unknown, and no close monitoring is being performed of animal behaviour to determine if any subtle changes occur during the early stages of infection before disease progression and gross physical changes that are associated with lameness becomes apparent. Therefore, in this transformational proof-of concept proposal we aim to fill current knowledge gaps by bringing together a unique and highly skilled project team from diverse backgrounds, gathered through the BBSRC Endemics Livestock Disease Initiative workshops for Priming Partnerships. Through our multi-disciplinary partnership, we will endeavour to lay foundations in the first year of research that will help improve the health and welfare of broiler chickens, so lameness and paralysis due to E. cecorum infection can be detected early, which will also help reduce antimicrobial usage during treatment and more successful treatment outcomes will help prevent large economic losses for farmers and the broiler industry. In this ambitious multi-pronged study, there will be three main components: pathogens genomics; transmission/persistence; and animal behaviour monitoring. Isolate genomics will help advance our understanding of E. cecorum pathogens. By performing detailed molecular characterisation, we will identify any genetic elementsthat have been acquired via transfer of mobile genes from other bacteria, particularly those living in the same environmental niche, resulting in increased virulence and a propensity of this bacterium, once a commensal, to cause endemic disease in poultry. Linkage of genes that cause resistance to antimicrobials, with key virulence determinants present in pathogenic variants, will help identify markers associated with pathogenic isolates that can be used for rapid detection on farms in future, using pen-side tests. In addition, groups of isolates found to be enriched with particular virulence elements that are from the same genetic lineages, will enable detection of E. cecorum types or clones associated with diseased birds in Great Britain. For identification of environmental reservoirs that enable transmission and persistence on farm, our plan is to perform in-depth sampling of surfaces, litter and water in positive houses and a negative control, at different periods of the production cycle, from five farms. An E. cecorum specific PCR will help identify presence, which will be verified in a subset by culture. Survival experiments will help distinguish how well E. cecorum survives in water, concrete and plastic that are common in poultry houses and part of our sampling protocol, especially upon exposure to biocides. Video sensors and associated analytical tools are an excellent way for monitoring animal behaviour closely, including detecting any subtle changes. By installing sensors on a subset of the farms and houses where environmental sampling will be performed, we will be able to use artificial intelligence to monitor flock performance and identify deviations from parameters such as climatic conditions, bird growth and water consumption. Deviations in infected flocks, will be verified by veterinary inspections and any correlation between early changes in behaviour and infection will be incorporated into future algorithms to help detect disease early.

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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