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

艾美耳球虫种群结构和遗传多样性的实时景观用于球虫病干预

• 批准号: 2725908
• 金额: --
• 依托单位: Royal Veterinary College
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2725908
• 关键词: Real; time; landscape; Eimeria; population

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.

球虫病是鸡中最重要的寄生虫病，影响生产力和福利，每年给全球家禽业造成超过100亿英镑的损失[1]。这种疾病是由原生动物寄生虫艾美耳球虫引起的，艾美耳球虫是顶复门中的一种寄生虫，是一组专性细胞内病原体，可引起严重的传染病，包括疟原虫和弓形虫。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驱动下，最近在南半球的大部分地区发现了三种首次在澳大利亚描述的新的神秘物种。这些研究发现，这三种疫苗都能够逃避商业抗球虫疫苗诱导的免疫杀伤[2]。如此出乎意料的复杂程度使得对艾美耳球虫的发生、丰度和种群结构的理解变得越来越重要。利用第三代长读段和易于部署的测序平台的优势，特别是来自纳米孔技术的优势，我们的目标是使用靶向和全基因组测序来定义寄生虫田间种群的实时景观和用于种群结构和疫苗遗传学分析的“艾美球虫基因组”工具。该项目将采取多学科的方法，目标如下：1.建立纳米孔测序的样品处理和文库制备方案。基因组DNA提取自培养物和代表五大洲的艾美耳球虫田间分离株。布莱克的小组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，以确定现场样本中的群体遗传和抗原多样性。将建立一个简化的基因组序列生成、组装和分析的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.开发一个关于疫苗有效性预测的综合机器学习模型。从基因型、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中提取和评估特征，以建立各种艾美耳球虫物种的预测模型。