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.

传染病动力学必然在生物范围内运作：病原体在宿主细胞和组织中复制；他们在宿主之间以及跨主机人群之间传播。因此，宿主 - 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，可以从分子到景观量表产生级联效应。同样，较大尺度的病原体成功的变化产生了选择性压力，可以反馈以塑造病原体种群遗传学。因此，将病原体动态跨生物量表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中的核心挑战。传染病动力学的多尺度模型寻求通过将代表从细胞量表到人群量表的宿主 - 病原体相互作用的机械模型联系起来来应对这一挑战。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近年来传染病建模的积极重点。到目前为止，这些理论创新尚未与经验数据生成相匹配，提供了记录在同一宿主 - 病原体系统中的感染过程的集成数据流，这些数据流跨组织量表始终收集。该不匹配限制了理论和数据之间的迭代相互作用。因此，尚未实现多尺度疾病模型的全部潜力。如果成功的话，这些方法可以提供预测自然宿主种群中新病原体变体的传播和持久性的工具，这些遗传或表型特征是直接紧迫的问题。在这个项目中，我们将使用其储层宿主中的非洲水牛（African Buffalo）作为模型系统，研究从基因组到景观量表的病毒动态。 FMDV是高度传染性的病毒，引起家用无染色的临床疾病，而野生动植物储层中的流行感染往往是亚临床的。 FMDV提出了一个出色的模型系统，用于合并多尺度疾病过程的理论和数据：（i）它们在普通的野生宿主中无处不在； （ii）病毒多样性很高，水牛种群中的三种不同的血清型基本上是独立的，并且在每种血清型中记录了良好的谱系； （iii）突变迅速产生，为分子痕迹和系统动力学分析提供了高分辨率； （iv）由于FMDV作为牲畜病原体的重要性，该系统具有良好的病毒培养方法，实验挑战，诊断和量化免疫反应的方法。在我们先前在南非克鲁格国家公园（KNP）的工作的基础上，我们将创建一个数据驱动的数学框架，将跨组织量表连接病毒动态，以测试宿主，人口和景观量表中的动态是否可以从病毒谱系中的表型变化中预测。我们将使用通过实验，观察性现场研究和系统动力学分析告知的数学模型在细胞，宿主，宿主，人群和景观量表处进行病毒动力学。在我们的模型中，我们将通过允许在较小尺度的动态来确定较大尺度的感染过程的参数来建立跨尺度的明确链接。