[22-EEID] Ecology and evolution of pathogen-microbiome-host interactions during population-level intermingling

[22-EEID] 种群水平混合过程中病原体-微生物组-宿主相互作用的生态学和进化

• 批准号: BB/Y006887/1
• 负责人: Joseph Neary
• 金额: $ 97.38万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY006887%2F1
• 关键词: 22; EEID; Ecology; evolution; pathogen

Commingling events occur when unfamiliar animals or people come together in a defined space and time with intensive and sustained contact. Commingling is associated with increased infectious pathogen transmission risk with possible global consequences, as the COVID-19 pandemic has highlighted. Commingling events in humans include mass-gathering events, back-to-school, air travel, incarceration, and mass migration. In livestock production, commingling routinely occurs in beef finishing systems and may occur on a national level when farmers rebuild herds following depopulation events, such as the recent foot-and-mouth disease outbreaks in the U.K. Commingling induces multi-level ecosystem disruption including perturbed social structure, co-circulating viral variants, host immune and physiological dyscrasia, and unstable microbial dynamics. We hypothesize that these interrelated processes create opportunity for viral transmission via three distinct mechanisms: 1) exposure of hosts to previously unseen viral variants; 2) host physiologic stress, including increased inflammation and immune system perturbation; and 3) ecological and evolutionary shifts in the microbiome. To test these hypotheses, we will perform controlled commingling trials using cattle relative to bovine coronavirus (BCV) transmission as a model system and pathogen, respectively. Using unique calf populations and facilities in the US and UK, we will generate highly-resolved time series datasets for BCV variant behaviour, host immune-inflammatory responses, and microbiome dynamics during commingling. Microbiome and BCV data will be analysed at the nucleotide level to uncover temporal variant- and strain-level ecology and evolution during commingling. We will model host immune-inflammatory responses as a multi-component system using specific markers and transcriptome analysis. We will then use these data to populate two novel temporal models of virus behaviour: first, an epidemiological risk factor model for disease during commingling using dynamic Bayesian network analysis; and second, a spatiotemporal SEIR pathogen transmission model that incorporates parameters for host immune response and microbiome eco-evolutionary shifts during commingling events.Intellectual MeritThe world is experiencing an inexorable trend towards increasingly frequent, intensive, large-scale commingling events among humans and animals. This has ramifications for viral transmission and variant evolution. There is an urgent need to understand the theoretical basis of virus dynamics specifically during these commingling events. A largely unexplored component of viral transmission during commingling is the host microbiome, which experiences dramatic shifts during commingling events. Our work has intellectual merit because it explicitly models these microbiome dynamics within a transmission and risk factor modelling framework. This will allow us to uncover organizing principles of viral transmission during commingling, which will advance theoretical understanding of virus behaviour at the variant level. Our work also improves and extends existing infectious disease modelling approaches by incorporating critical commingling and microbiome dynamics with temporal and host stochasticity. Finally, we would provide the scientific community with a highly-resolved empirical dataset as well as a novel study platform for future research on infectious disease dynamics during commingling events.Broader ImpactsWe propose to develop an integrated program that will train veterinary students in research experiences and advanced epidemiological methods that will help build the next generation of agricultural leaders and researchers. We also propose a series of Bioinformatics Workshops to engage agricultural researchers in primary analysis of genomic data. Our project outcomes will have immediate relevance to livestock husbandry practices.

当不熟悉的动物或人在一个确定的空间和时间内以密集和持续的接触聚集在一起时，就会发生混合事件。正如COVID-19大流行所强调的那样，混合与传染性病原体传播风险增加有关，可能导致全球后果。人类的混合事件包括大规模聚集事件，返校，航空旅行，监禁和大规模迁移。在畜牧业生产中，混养通常发生在牛肉肥育系统中，也可能发生在国家一级，即农民在人口减少事件后重建牛群时，例如最近在英国爆发的口蹄疫。混养会导致多层次的生态系统破坏，包括社会结构的扰动、共循环的病毒变体、宿主免疫和生理性恶液质以及不稳定的微生物动力学。我们假设这些相互关联的过程通过三种不同的机制为病毒传播创造了机会：1）宿主暴露于以前未见过的病毒变体; 2）宿主生理应激，包括炎症增加和免疫系统干扰; 3）微生物组的生态和进化变化。为了验证这些假设，我们将使用牛相对于牛冠状病毒（BCV）传播分别作为模型系统和病原体进行对照混养试验。利用美国和英国独特的小牛种群和设施，我们将为混合期间的BCV变异行为、宿主免疫炎症反应和微生物组动态生成高分辨率的时间序列数据集。微生物组和BCV数据将在核苷酸水平上进行分析，以揭示混合过程中的时间变异和菌株水平的生态学和进化。我们将使用特异性标记和转录组分析将宿主免疫炎症反应建模为多组分系统。然后，我们将使用这些数据填充两个新的时间模型的病毒行为：第一，流行病学风险因素模型的疾病在混合使用动态贝叶斯网络分析;第二，时空SEIR病原体传播模型，该模型结合了混合事件期间宿主免疫反应和微生物组生态进化变化的参数。知识产权世界正在经历一个不可阻挡的趋势，人类和动物之间密集的大规模混合事件。这对病毒传播和变异进化有影响。有一个迫切需要了解病毒动力学的理论基础，特别是在这些混合事件。在混合期间病毒传播的一个很大程度上未被探索的组成部分是宿主微生物组，其在混合事件期间经历了戏剧性的变化。我们的工作具有智力价值，因为它在传播和风险因素建模框架内明确地模拟了这些微生物组动态。这将使我们能够揭示病毒在混合过程中传播的组织原则，这将促进对变异水平病毒行为的理论理解。我们的工作还通过将关键混合和微生物组动态与时间和宿主随机性相结合，改进和扩展了现有的传染病建模方法。最后，我们将为科学界提供一个高分辨率的经验数据集，以及一个新的研究平台，为未来的研究传染病动力学在混合事件。更广泛的影响我们建议制定一个综合计划，将培训兽医学生的研究经验和先进的流行病学方法，这将有助于建立下一代的农业领导者和研究人员。我们还建议举办一系列生物信息学研讨会，让农业研究人员参与基因组数据的初步分析。我们的项目成果将与畜牧业实践直接相关。