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Worms and Bugs - Quantifying Infection Dynamics in Microcosms

蠕虫和臭虫 - 量化微观世界中的感染动态

基本信息

批准号：
BB/I012222/1
负责人：
Olivier Restif
金额：
$ 34.56万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2012
资助国家：
英国
起止时间：
2012 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FI012222%2F1
关键词：
Worms Bugs Quantifying Infection Dynamics

项目摘要

Despite continual progress in medicine, infectious diseases pose an unabated threat not only to humans but also to domestic and wild animals. Epidemiologists rely increasingly on mathematical models to analyse and predict the outcome of epidemic outbreaks. However, those models are still far from perfect and are based on many assumptions that cannot always be validated. One of the remaining black boxes in our understanding of infectious disease dynamics is transmission. On the one hand, new technologies have allowed microbiologists to gain increasingly detailed knowledge of the molecular interactions between pathogens and their hosts, informing on the within-host dynamics of infection. On the other hand, the roles of environment and host behaviour on the spread of epidemics is better understood, with the recent hindsight from SARS, pandemic influenza or foot-and-mouth disease outbreaks. But there remains a major gap between those two scales. In particular, we are still far from being able to predict the epidemic potential of a pathogen just based on measures of its growth and spread within an individual host. Reconciling those two levels is also an essential step to improve our understanding of pathogen evolution: because different factors may favour within-host growth and between-host transmission, we are likely to misjudge the selective pressures acting on the whole life-cycle of pathogens. This can have practical implications for the management of vaccination and drugs. In order to help fill those gaps, I propose to set up a new experimental host-pathogen system where individual-level and population dynamics can be measured and used to design and validate integrated mathematical models. Experiments on animals, while essential to gain specific knowledge on chosen pathogens, are limited in scope by technical and ethical issues. My project will use the free-living nematode worm Caenorhabditis elegans, which has been studied by biologists throughout the world for half a century. The worms can be infected in the lab with various microbes, either specific parasites of C. elegans or foodborne pathogens of humans and animals such as Salmonella. Using microscopes, it is possible to keep track of the numbers of infected and uninfected nematodes in a microcosm (an experimental population maintained in a large Petri dish), but also measure the development of infection and its effects in individual worms. Data from these experimental epidemics will be used to design and parameterise mathematical models that simulate population dynamics. The models can then be used to predict the outcomes of different experiments, which can then be carried out in the lab to validate the models. The aim is to establish the quantitative links between individual-level measurements and epidemic spread. For example, if we observe that variations in resources affect the ability of worms to resist or survive infection, can we predict how that will modify the circulation of the pathogen in the population? We will also assess the competitive abilities of different pathogen lines: some pathogens might have a higher growth rate within individual hosts but a lower transmission ability (for example if they kill their host too quickly). Which genotype 'wins' (i.e. spreads across a host population) will depend on a combination of factors, which can be measured and combined into a mathematical framework. These questions are important to understand the ecology and evolution of infectious diseases in natural populations. While they have been studied theoretically for many years, the application to real systems has remained difficult because of our lack of understanding of the detailed mechanisms of infection dynamics at the interface of individuals and populations. This project offers a unique opportunity to reconcile those different levels of investigation and test some fundamental assumptions of mathematical models that had not been validated before.

尽管医学不断进步，传染病不仅对人类，而且对家畜和野生动物都构成了有增无减的威胁。流行病学家越来越依赖数学模型来分析和预测疫情暴发的结果。然而，这些模型仍然远远不是完美的，而且是基于许多并不总是得到验证的假设。在我们对传染病动力学的理解中，剩下的一个黑匣子就是传播。一方面，新技术使微生物学家能够越来越详细地了解病原体与其宿主之间的分子相互作用，从而了解宿主内部的感染动态。另一方面，随着最近SARS、大流行性流感或口蹄疫暴发的后见之明，人们更好地了解了环境和宿主行为在流行病传播中的作用。但这两个规模之间仍有很大差距。特别是，我们仍然远远不能仅仅基于对病原体在单个宿主内的生长和传播的测量来预测病原体的流行潜力。协调这两个水平也是提高我们对病原体进化理解的关键一步：因为不同的因素可能有利于宿主内的生长和宿主之间的传播，我们很可能误判作用于病原体整个生命周期的选择压力。这可能会对疫苗接种和药物管理产生实际影响。为了帮助填补这些空白，我建议建立一个新的实验寄主-病原体系统，其中可以测量个体水平和种群动态，并用于设计和验证集成的数学模型。在动物身上进行实验，虽然对于获得关于选定病原体的具体知识是必不可少的，但其范围受到技术和伦理问题的限制。我的项目将使用自由生活的线虫线虫，这种线虫已经被世界各地的生物学家研究了半个世纪。这些蠕虫在实验室中可以被各种微生物感染，要么是线虫的特定寄生虫，要么是人和动物的食源性病原体，如沙门氏菌。使用显微镜，可以跟踪微型世界(保存在大型培养皿中的实验种群)中感染和未感染线虫的数量，但也可以测量感染的发展及其对单个蠕虫的影响。来自这些实验性流行病的数据将被用来设计和参数化模拟人口动态的数学模型。然后，这些模型可以用来预测不同实验的结果，然后可以在实验室进行验证模型。其目的是建立个体水平测量和疫情传播之间的量化联系。例如，如果我们观察到资源的变化会影响蠕虫抵抗或存活感染的能力，我们能预测这将如何改变病原体在种群中的循环吗？我们还将评估不同病原菌株的竞争能力：一些病原体可能在单个宿主内具有较高的生长速度，但传播能力较低(例如，如果它们太快杀死宿主)。哪种基因“获胜”(即在宿主群体中传播)将取决于各种因素的组合，这些因素可以被测量并合并到一个数学框架中。这些问题对于了解自然人群中传染病的生态学和进化很重要。虽然它们在理论上已经研究了很多年，但由于我们缺乏对个体和群体界面感染动力学的详细机制的了解，将其应用到实际系统中仍然是困难的。这一项目提供了一个独特的机会，可以协调这些不同的调查水平，并测试一些以前没有得到证实的数学模型的基本假设。