Report

Teaser, summary, work performed and final results

Periodic Reporting for period 2 - MathModExp (The Evolution of Competition and Cooperation: how polymorphisms in microbial populations optimise virulence and mediate drug resistance)

Teaser

Microbes form intricate communities where multiple strains and species communicate, cooperate and compete, they can cause life-threatening diseases and destroy our food sources. Metabolism is key to these interactions, yet the way microbes acquire and utilise nutrients is...

Summary

Microbes form intricate communities where multiple strains and species communicate, cooperate and compete, they can cause life-threatening diseases and destroy our food sources. Metabolism is key to these interactions, yet the way microbes acquire and utilise nutrients is often overlooked in evolutionary studies of pathogenicity, virulence and antibiotic resistance. My project address this by quantifying how microbial community composition is determined by the metabolism, genetics and physiology of individual players, establishing principles by which microbial composition affects virulence and antimicrobial resistance.

Competition for resources is the most basic of ecological interactions, fundamental because one cell directly impacts the fitness of others. It is only by incorporating nutrient acquisition and utilisation into studies of virulence and antibiotic resistance that we can predict, and ultimately control, the evolutionary response of microbes to resource stresses, antimicrobials and host defenses.

The project addresses two outstanding problems:
Challenge one: Pathogens must acquire nutrients from their hosts, but what combination of different resource acquisition and utilisation strategies maximise population success and, therefore, virulence?
Challenge two: Antibiotics can perturb the composition of polymicrobial communities from susceptible to resistant species but how is this shift mediated by resource utilisation strategies?

The project integrates empirical data and theory with concepts from ecology and evolutionary dynamics. We are formulating new theoretical tools that allow us to make predictions that are subsequently fully challenged by data, both in vitro and in vivo. We are exploiting advances in the molecular genetics of important plant and human pathogens and we use them to synthesise polymorphic microbial populations and polymicrobial communities. This will enable us to understand what makes microbes so relisient to the challenges they face.

Work performed

The majority of work for this reporting period falls under Challenge 1. We successfully developed a novel synthetic cooperative system using the rice blast fungus Magnaporthe oryzae, which has enabled the following scientific discoveries.

1. We hypothesized that while a monomorphic, co-operator only, population maximises virulence in a single-trait cooperative system, a mixture of co-operators and cheats maximises population virulence in a multi-trait system. To examine why multi-trait cooperative systems behave differently from single-trait systems we developed, simulated and analysed a mathematical model of invertase production. We reasoned that alongside the first social dilemma, namely public goods production, M.oryzae faces a second dilemma of self-restraint or “tragedy of the commons”. Mathematical predictions supported this hypothesis and the theoretical results were subsequently verified experimentally. The results have been published in Lindsay et al 2016 eLife, demonstrating the broader significance of our findings for anti-virulence disease management strategies.

2. We have also successfully classified the impact variation in population density and spatial structure has on the frequency of cooperation in multi-trait cooperative systems in vitro. We have shown that while high population densities support cheats in public goods production systems, high population density can support co-operators in multi-trait systems where public goods production is linked to the efficiency of resource utilisation. The work was published in Lindsey et al 2017 ISME J and is central to our understanding of ecological and epidemiological processes from nutrient recycling to antibiotic resistance. Using a genuine interplay between mathematical modelling and microbial experiments with Saccharomyces cerevisiae we provided the first experimental evidence that cooperation can be favoured at high population densities in spatially structured environments. Furthermore using mathematical models we also identified a mechanistic explanation as to why this result has so far been elusive. Namely we showed that that prior empirical procedures did not capture the extent of environmental variation required, in theory, to support cooperation.

3. We have also begun to classify the menagerie of likely interactions between cooperation in two different metabolic traits. Our findings have been reviewed in Nature E&E and we are currently working on addressing the reviewer’s comments.

Prior to the start of the work on Challenge 2 (3rd January 2018), we conducted pilot research which has recently been published in Beardmore et al 2018 Nature E&E and Reding-Roman et al 2017 Nature E&E. We have shown that single species dose response is a poor predictor of multi-species community dynamics because it cannot foresee the tipping points that cause irreversible changes in resistance that persists, even when treatment stops.

Final results

We developed a novel multi-trait cooperative system using rice blast fungus M.oryzae that has enabled us, for the first time, to study the effect of multi-trait cooperation on the evolution of virulence (Challenge 1). We fully expect that all objectives under this challenge will be met by the end of the project and that we will develop a new understanding of the relationships between spatial structure and virulence, transmission and virulence and population density and virulence in multi-trait infection systems. This will enable us to improve rationales for virulence reduction strategies to manage disease.

We have only just begun working on Challenge 2 but we also expect to fully meet the its objectives.