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

球虫病是鸡最重要的寄生虫病，影响鸡的生产和福利，每年给全球养禽业造成超过100亿GB的损失。这种疾病是由原生动物寄生虫艾美耳球虫引起的，艾美耳球虫是尖端复合门中的一种寄生虫，是一组专有的细胞内病原体，可导致包括疟原虫和弓形虫在内的严重传染病。甚至有公认的鸡感染艾美耳球虫物种。然而，在基因组可塑性和可能的物种间杂交的推动下，澳大利亚首次描述的三个新的神秘物种最近在南半球的大部分地区被发现。这些研究发现，这三种疫苗都能够逃脱商业抗球虫疫苗诱导的免疫杀伤[2]。如此令人意想不到的复杂性使得了解艾美耳球虫的发生、丰度和种群结构变得越来越重要。利用第三代长阅读和易于部署的测序平台的优势，特别是利用纳米孔技术，我们的目标是使用定向和全基因组测序来定义寄生虫田间种群的实时格局，并使用‘Eimeria-ome’工具进行种群结构和疫苗遗传分析。该项目将采取多学科方法，目标如下：1.建立纳米孔测序的样品处理和文库制备方案。从培养物和代表五大洲的艾美耳球虫田间分离株中提取基因组DNA。布莱克的团队将被用来测试和完善协议。2.优化生物信息学分析工作流程，以表征田间样本的群体遗传和抗原性多样性。将建立用于基因组测序生成、组装和分析的简化生物信息学工作流程，以促进纳米孔测序数据的实时分析。3.建立了疫苗效力预测的综合机器学习模型。将提取和评估来自基因型、靶向抗原多样性、测序、亚细胞定位和多组学疫苗反应数据集的特征，以建立不同艾美耳球虫物种的预测模型。

Coccidiosis is the most important parasitic disease in chickens, which impacts onproductivity and welfare and costs the global poultry industry over £10 billion everyyear [1]. The disease is caused by protozoan parasites Eimeria, a genus of parasiteswithin the phylum Apicomplexa, a group of obligate intracellular pathogens that cancause serious infectious diseases including Plasmodium, and Toxoplasma. There areseven well recognised chicken infecting Eimeria species. However, driven by genomicplasticity and possible inter-species hybridisations, three new cryptic species that werefirst described in Australia have recently detected across much of the southernhemisphere. These studies have found all three to be capable of escape from immunekilling induced by commercial anticoccidial vaccines [2]. Such unexpected levels ofcomplexity have made the understanding of Eimeria occurrence, abundance andpopulation structure increasingly important. Harnessing the advantages of thirdgeneration long reads and easy to deploy sequencing platforms, particularly fromnanopore technology, we aim to use targeted and genome-wide sequencing to definea real-time landscape of parasite field populations and 'Eimeria-ome' tools forpopulation structure and vaccinology genetic analyses. The project will take amultidisciplinary approach with the following objectives: 1. Establish sampleprocessing and library preparation protocols for nanopore sequencing. Genomic DNAextracted from culture and Eimeria field isolates representing five continents fromProf. Blake's group will be used to test and refine the protocols. 2. Optimisebioinformatic analyse workflows to characterise population genetic and antigenicdiversity in field samples. A streamlined bioinformatics workflow for genome sequencegeneration, assembly and analysis will be established to facilitate real-time analysis ofnanopore sequencing data. 3. Develop an integrative machine learning model onvaccine effectiveness predictions. Features from genotypes, targeted antigen diversitysequencing, subcellular localisations and multi-omics vaccine responses datasets willbe extracted and evaluated to build a predictive model for various Eimeria species.