Real-time landscape of Eimeria population structure and genetic diversity for coccidiosis intervention

Coccidiosis is the most important parasitic disease in chickens, which impacts on
productivity and welfare and costs the global poultry industry over £10 billion every
year [1]. The disease is caused by protozoan parasites Eimeria, a genus of parasites
within the phylum Apicomplexa, a group of obligate intracellular pathogens that can
cause serious infectious diseases including Plasmodium, and Toxoplasma. There are
seven well recognised chicken infecting Eimeria species. However, driven by genomic
plasticity and possible inter-species hybridisations, three new cryptic species that were
first described in Australia have recently detected across much of the southern
hemisphere. These studies have found all three to be capable of escape from immune
killing induced by commercial anticoccidial vaccines [2]. Such unexpected levels of
complexity have made the understanding of Eimeria occurrence, abundance and
population structure increasingly important. Harnessing the advantages of third
generation long reads and easy to deploy sequencing platforms, particularly from
nanopore technology, we aim to use targeted and genome-wide sequencing to define
a real-time landscape of parasite field populations and 'Eimeria-ome' tools for
population structure and vaccinology genetic analyses. The project will take a
multidisciplinary approach with the following objectives: 1. Establish sample
processing and library preparation protocols for nanopore sequencing. Genomic DNA
extracted from culture and Eimeria field isolates representing five continents from
Prof. Blake's group will be used to test and refine the protocols. 2. Optimise
bioinformatic analyse workflows to characterise population genetic and antigenic
diversity in field samples. A streamlined bioinformatics workflow for genome sequence
generation, assembly and analysis will be established to facilitate real-time analysis of
nanopore sequencing data. 3. Develop an integrative machine learning model on
vaccine effectiveness predictions. Features from genotypes, targeted antigen diversity
sequencing, subcellular localisations and multi-omics vaccine responses datasets will
be extracted and evaluated to build a predictive model for various Eimeria species.