Award details

Worms and Bugs - Quantifying Infection Dynamics in Microcosms

Reference	BB/I012222/1
Principal Investigator / Supervisor	Dr Olivier Restif
Co-Investigators / Co-Supervisors
Institution	University of Cambridge
Department	Veterinary Medicine
Funding type	Research
Value (£)	271,251
Status	Completed
Type	Research Grant
Start date	06/02/2012
End date	05/08/2014
Duration	30 months

Abstract

This proposal is for a 30-month systems-based study with three main components: - Establish experimental populations of the nematode Caenorhabditis elegans infected with various pathogens, and measure the dynamics of infection at the individual and population levels. - Design and validate mathematical models for the population dynamics of nematodes and their pathogens. - Fit these mathematical models to experimental data using Bayesian statistical models. Nematode populations will be maintained on agar plates and fed on a choice of non-pathogenic bacteria. The number of individuals in different development stages (larvae and adults) can be assessed using a stereomicroscope. Individual life-history traits (fecundity and longevity) can be measured by isolating nematodes. As a first step, we will parameterise simple population dynamic models under different environmental conditions (feed and temperature) in different genotypes of C. elegans. We will then inoculate nematodes in isolation or in small groups with selected pathogens, namely: Microbacterium nematophilum (bacteria), Nematocida parisii (microsporidia) and Salmonella enterica Typhimurium (bacteria). By direct microscopy observation (including fluorescent markers), we will determine how infection affects individual life-history traits in different conditions and will measure transmission between individuals. Different strains of C. elegans will be compared. The data will be used to design mathematical models for the population dynamics of different host-pathogen combinations. The key stage of the project will aim to validate and parameterise those models by monitoring the demography and spread of infection in nematode populations. Individual measurements will provide prior parameter estimates for Bayesian models. Once validated, those models will be used to formulate predictions for experimental competition between multiple strains of nematodes or pathogens.

Summary

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.

Impact Summary

Throughout this project, I will take an active role to achieve societal impacts as explained in the attached Impact Statement. Here I outline who will benefit, how they will benefit and what I will do to achieve impact. (a) Who: general public. How: Restore confidence in quantitative research on public health and environment. What: Demonstrate in a visual way how simple experiments can be combined with mathematical models to develop analytical and predictive tools for infectious disease dynamics. (b) Who: Schoolchildren. How: Education in health and environment. What: Work with local association to educate students about the diverse functions of microorganisms. (c) Who: Policy makers. How: Use of improved and more reliable mathematical models for infectious disease dynamics. What: In the long run, contribute towards the refinement and improvement of evidence-based policies for the control of infectious diseases.

Committee	Research Committee B (Plants, microbes, food & sustainability)
Research Topics	Microbiology, Systems Biology
Research Priority	Systems Approach to Biological research
Research Initiative	X - not in an Initiative
Funding Scheme	X – not Funded via a specific Funding Scheme

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