21-EEID US-UK Collab: Multi-scale infection dynamics from cells to landscapes: foot-and-mouth disease viruses in African buffalo

21-EEID 美英合作：从细胞到景观的多尺度感染动态：非洲水牛的口蹄疫病毒

• 批准号: BB/X006085/1
• 负责人: Simon Gubbins
• 金额: $ 152.75万
• 依托单位: The Pirbright Institute
• 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%2FX006085%2F1
• 关键词: 21; EEID; US; UK; Collab

Infectious disease dynamics necessarily operate across biological scales: pathogens replicate within host cells and tissues; they transmit among hosts, and across host populations. As such, functional changes in host-pathogen interactions, such as those affecting pathogen vital rates or host immune responses, can generate cascading effects from molecular to landscape scales. Similarly, variation in pathogen success at larger scales generates selective pressures that feed back to shape pathogen population genetics. Linking pathogen dynamics across biological scales is thus critical to understanding evolutionary trajectories of host-pathogen systems and represents a central challenge in disease ecology. Multi-scale models of infectious disease dynamics seek to address this challenge by linking mechanistic models representing host-pathogen interactions from cellular to population scales. Developing the mathematical tools for connecting dynamic processes operating at vastly different temporal and spatial scales has been an active focus in infectious disease modelling over recent years. These theoretical innovations have so far not been matched by empirical data generation, providing integrated data streams documenting infection processes in the same host-pathogen system, collected consistently across organizational scales. This mismatch has limited the iterative interplay between theory and data. As such, the full potential of multi-scale disease models has not been realized. If successful, these approaches could provide tools for predicting the spread and persistence of new pathogen variants in natural host populations from variant genetic or phenotypic traits - a question of immediate urgency. In this project we will investigate viral dynamics from genomic to landscape scales using a suite of foot-and-mouth disease viruses (FMDVs) in their reservoir host, African buffalo, as a model system. FMDVs are highly contagious viruses that cause clinical disease in domestic ungulates, while endemic infections in their wildlife reservoir tend to be subclinical. FMDVs present an excellent model system for merging theory and data on multi-scale disease processes: (i) they are ubiquitous in a common wild host; (ii) viral diversity is high, with three distinct serotypes circulating in the buffalo population essentially independently, and well-differentiated lineages documented within each serotype; (iii) mutations accrue rapidly, providing high resolution for molecular tracing and phylodynamic analysis; and (iv) due to FMDV's importance as a livestock pathogen, the system is tractable with well established methods for virus culture, experimental challenges, diagnostics and quantifying immune responses. Building on our previous work in South Africa's Kruger National Park (KNP), we will create a data-driven mathematical framework linking viral dynamics across organizational scales, to test if and how dynamics within hosts, at population and landscape scales can be predicted from phenotypic variation among viral lineages. We will investigate viral dynamics at cellular, within-host, population and landscape scales using mathematical models informed by experiments, observational field studies and phylodynamic analysis. In our models, we will establish explicit linkages across scales by allowing dynamics at the smaller scales to determine parameters for infection processes at larger scales.

传染病动态必然在生物尺度上发挥作用：病原体在宿主细胞和组织内复制；它们在宿主之间以及宿主群体之间传播。因此，宿主与病原体相互作用的功能变化，例如影响病原体生命率或宿主免疫反应的功能变化，可以产生从分子到景观尺度的级联效应。同样，病原体在更大范围内成功的变化会产生选择性压力，从而影响病原体种群遗传学。因此，将跨生物尺度的病原体动态联系起来对于理解宿主-病原体系统的进化轨迹至关重要，并且代表了疾病生态学的核心挑战。传染病动力学的多尺度模型试图通过将代表从细胞尺度到群体尺度的宿主-病原体相互作用的机制模型联系起来来应对这一挑战。近年来，开发用于连接在截然不同的时间和空间尺度上运行的动态过程的数学工具一直是传染病建模的一个积极焦点。迄今为止，这些理论创新尚未与经验数据生成相匹配，无法提供记录同一宿主病原体系统中感染过程的集成数据流，并在整个组织范围内一致收集。这种不匹配限制了理论和数据之间的迭代相互作用。因此，多尺度疾病模型的全部潜力尚未发挥出来。如果成功，这些方法可以提供工具，根据变异遗传或表型特征预测新病原体变异在自然宿主群体中的传播和持久性——这是一个迫在眉睫的问题。在这个项目中，我们将使用其储存宿主非洲水牛中的一套口蹄疫病毒（FMDV）作为模型系统，研究从基因组到景观尺度的病毒动态。口蹄疫病毒是一种高度传染性的病毒，会在家养有蹄类动物中引起临床疾病，而野生动物宿主中的地方性感染往往是亚临床的。口蹄疫病毒提供了一个优秀的模型系统，用于融合多尺度疾病过程的理论和数据：（i）它们在常见的野生宿主中普遍存在； (ii) 病毒多样性很高，三种不同的血清型基本上独立地在水牛种群中传播，并且每种血清型中记录了分化良好的谱系； (iii) 突变迅速积累，为分子追踪和系统动力学分析提供高分辨率； (iv) 由于口蹄疫作为牲畜病原体的重要性，该系统易于通过完善的病毒培养、实验挑战、诊断和量化免疫反应方法进行处理。基于我们之前在南非克鲁格国家公园（KNP）所做的工作，我们将创建一个数据驱动的数学框架，将跨组织规模的病毒动态联系起来，以测试是否以及如何从表型预测宿主内、种群和景观规模的动态。病毒谱系之间的变异。我们将使用由实验、实地观察研究和系统动力学分析提供的数学模型，在细胞、宿主内、群体和景观尺度上研究病毒动态。在我们的模型中，我们将通过允许较小尺度的动态确定较大尺度的感染过程的参数来建立跨尺度的明确联系。