Understanding host-pathogen interactions using a new synthetic theoretical framework for organismal nutrition

Lead Research Organisation: National Oceanography Centre
Department Name: Science and Technology

Abstract

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Technical Summary

Many factors can impact the outcome of infections, but the role of host nutrition is one of the most widely recognised, with both over- and under-nutrition affecting the susceptibility to infectious disease.

However, whilst many studies have now demonstrated nutrition-driven effects on host-parasite interactions, we have only a limited understanding of the mechanisms underpinning them.

Here, we propose to address this issue by using a novel combination of in vivo, in vitro and in silico approaches and a model host-pathogen system comprising an insect crop pest and 4 entomopathogenic bacteria.

We will use the new Geometric Stoichiometry (GS) framework (Anderson 2020) to model the dynamic interaction between the nutritional requirements, and consequences for growth and mortality, of the host and pathogen. This will be underpinned by data from experiments that will quantify host utilisation of different nutrients and the time-varying metabolic budgets of both the host and its pathogens (singly and in combination).

We will first construct a metabolic budget for the host by quantifying intake, growth, respiration and excretion of the host, from which we can understand the impact on the host, at a physiological level, of diets varying in their amounts and ratios of protein and carbohydrate. We will also quantify the consequences for this budget of mobilising an immune response against non-lethal infections with four bacteria.

We have recently developed synthetic bloods with the same nutritional properties as the bloods of insects feeding on these different diets (Wilson et al. 2020). We will use these to help construct metabolic budgets for the four bacteria growing in vitro in the absence of any host immune response.

Finally, we will use these host and pathogen metabolic budgets developed using GS to make a priori predictions about the outcome of the host-pathogen interactions, which will then be tested empirically using infection experiments.

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