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

羊足病的致病因子结节双杆菌中有过多的毒力因子，但这些并没有明确界定良性和毒力菌株。该疾病本身在不同的气候中是地方性的或流行的，这表明其病因复杂。我们认为，只有用非培养方法研究结节菌，我们才能了解定植、细菌毒力和绵羊疾病发作之间的动态关系。鉴于结节棘球蚴的持久性，了解这种关系对于制定疾病控制策略（包括更好的疫苗）至关重要。本博士将探索羊群中结瘤单胞菌群落的毒力因子范围，以验证在地方性情况下结瘤单胞菌群落在全国分布的假设，以及可以通过在国家一级对菌株进行有针对性的管理来获得控制的假设。实验方法学生将从英格兰各地的农民那里获取棉签样本，这些农民还将发送他们的羊的数据，包括估计的脚印和ID水平。方法和研究设计使用流行病学原理设计采样方案和研究，从第1年和第2年响应我们最近的研究（700名农民）的有代表性的英国羊农中收集拭子样本，进行研究并接收拭子样本（24个月）2。2 .学习分子方法，调查收集的结瘤单胞菌菌群（30个月）的血清组和毒力因子。使用模型来检验血清组和毒力与英格兰羊的空间聚集和农民在生物安全和购买羊方面的行为有关的假设。（36个月）确定最流行的血清群，并检验血清群特异性疫苗可用于控制英格兰足癣的假设。最终目的是确定结节性丹梭菌的10个血清群的相对比例，并探讨是否可以使用较少血清群的疫苗来控制绵羊的足疾。分子训练学生将从储存的拭子上的指间物质中提取DNA。他们将通过qPCR确定所有血清组和每个血清组的毛负荷，并使用特定引物调查毒力因子的存在（如fimA， aprV2; Keenan et al., 2011）。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状态模型和潜在类模型，允许预测和推断，以确定结节棘球蚴毒力因素的群落模式和结节棘球蚴在全国的分布。独创性：以前从未使用流行病学数据对国家结索菌群落进行细菌菌株分析研究，这对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，并且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：如何分析来自社区菌株分析的数据，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.