What really causes footrot in sheep: a national cohort study of the microbial diversity of Dichelobacter nodosus in sheep in sickness and in health

绵羊蹄疾病的病原菌结节双歧杆菌中含有过多的毒力因子，但这些毒力因子并没有明确界定良性和强毒菌株。这种疾病本身在不同的气候中是地方性的或流行的，这表明病原学很复杂。我们认为，只有通过使用非培养方法研究结节螺旋藻，我们才能了解绵羊定殖率、细菌毒力和疾病发作之间的动态关系。考虑到结节螺旋体的持久性，了解这种关系对于制定疾病控制策略，包括更好的疫苗是至关重要的。本博士将探索在羊群中结节圆线虫群落的毒力因子范围，以检验这样的假设：在流行情况下，结节圆线虫种群分布在全国，并且可以通过在国家层面上有针对性地管理菌株来获得控制。实验方法学生将从英格兰各地的农民那里获取拭子样本，这些农民也将从他们的绵羊身上发送数据，包括估计的食物和ID水平。方法和研究设计1。利用流行病学原理设计抽样方案并进行研究，在第一年和第二年从对我们最近的研究(700名农民)做出回应的有代表性的英国养羊者那里收集拭子样本，进行研究并接收拭子样本(24个月)2.学习分子方法并调查收集到的节结线虫群落中的血清群和毒力因素(30个月)3.使用模型检验血清群和毒力与英国绵羊的空间聚集性和从生物安全和绵羊购买方面的农民行为有关的假设。(36个月)4.确定最流行的血清组，并测试一种针对血清组的疫苗可以用来控制英格兰蹄疾的假设。最终目的是确定10个结节蠕形虫血清组的相对比例，并调查是否可以使用血清组较少的疫苗来控制绵羊的蹄疾。分子培训学生将从储存的棉签上的指间物质中提取DNA。他们将通过qPCR鉴定所有血清组和每个血清组的菌毛载量，并使用特定的引物来调查毒力因子的存在(例如FimA、aprV2；统计和流行病学模型：学生将在贝叶斯框架中使用混合效应多态模型和潜在类别模型，该模型允许预测和推断，以识别节结线虫毒力因子的群落内的模式和节结线虫在全国的分布。原始性：以前从未使用流行病学数据通过对细菌菌株的分析来研究节状线虫的全国群落，这对于任何疾病来说都是非常新颖的。困难：发展分子生物学的技能需要时间，在一个样本中有几个菌株将需要考虑如何总结数据以供进一步分析。挑战：如何分析来自群落菌株分析的数据，以便它们可以用于研究的统计建模。

There are a plethora of virulent factors in Dicelobacter nodosus, the causal agent of ovine footrot, but these do not clearly define benign and virulent strains. The disease itself is endemic or epidemic in different climates suggesting a complex aetiology. We consider that only by studying D. nodosus using non-culture methods will we understand the dynamic relationship between colonisation, bacterial virulence and disease episodes in sheep. Given the persistent nature of D. nodosus, understanding this relationship is essential to develop strategies for control of disease, including better vaccines. This PhD will explore the range of virulence factors in the community of D. nodosus in flocks of sheep, to test the hypothesis that in endemic situations a community of D. nodosus strains are distributed nationally and that control can be obtained by targeted management of strains at a national level.Experimental approachesThe student will source swab samples from farmers throughout England who will also send data from their sheep, including estimated level of footrot and ID.Methodology and study design1. Use epidemiological principles to design the sampling protocol and study to collect a sample of swabs from a representative number of English sheep farmers who responded to our recent study (700 farmers) in years 1 and 2, conduct the study and receive the swab samples (24 months)2. Learn molecular methods and investigate serogroup and virulence factors in the community of D. nodosus strains collected (30 months)3. Use modelling to test the hypothesis that serogroup and virulence are associated with spatial clustering of sheep in England and farmer behaviour in terms of biosecurity and purchase of sheep. ( 36 months)4. Identify the most prevalent serogroups and test the hypothesis that a serogroup specific vaccine could be used to control footrot in England. The ultimate aim is to identify the relative proportion of to 10 serogroups of D. nodosus and to investigate whether a vaccine with fewer serogroups could be used to control footrot in sheep.Molecular trainingThe student will extract DNA from interdigital matter on stored swabs. They will identify all serogroups and fimbrial load of each serogroup by qPCR and use specific primers to investigate the presence of virulence factors (e.g. fimA, aprV2; Keenan et al., 2011).Statistical and epidemiological modellingThe student will use mixed effect multistate models and latent class models in a Bayesian framework, that allows prediction and inference, to identify patterns within the community of D. nodosus virulence factors and distribution of D. nodosus nationally.Originality: The national community of D. nodosus has never been studied before with analysis of strains of bacteria using epidemiological data, this is highly novel for any disease.Difficulties: Developing skills to do the molecular biology will take time and having several strains in a sample will require consideration on how to summarise the data for further analysis.Challenges: How to analyse the data from the community strain analysis so that they can be used in statistical modelling of the study.