A cornerstone of classical virulence evolution theories is the assumption that pathogen growth rate is positively correlated with virulence, the amount of damage pathogens inflict on their hosts. Such theories are key for incorporating evolutionary principles into sustainable disease management strategies. Yet, empirical evidence raises doubts over this central assumption underpinning classical theories, thus undermining their generality and predictive power. In this paper, we identify a key component missing from current theories which redefines the growth-virulence relationship in a way that is consistent with data. By modifying the activity of a single metabolic gene, we engineered strains of Magnaporthe oryzae with different nutrient acquisition and growth rates. We conducted in planta infection studies and uncovered an unexpected non-monotonic relationship between growth rate and virulence that is jointly shaped by how growth rate and metabolic efficiency interact. This novel mechanistic framework paves the way for a much-needed new suite of virulence evolution theories.

Developing mathematical models to accurately predict microbial growth dynamics remains a key challenge in ecology, evolution, biotechnology, and public health. To reproduce and grow, microbes need to take up essential nutrients from the environment, and mathematical models classically assume that the nutrient uptake rate is a saturating function of the nutrient concentration. In nature, microbes experience different levels of nutrient availability at all environmental scales, yet parameters shaping the nutrient uptake function are commonly estimated for a single initial nutrient concentration. This hampers the models from accurately capturing microbial dynamics when the environmental conditions change. To address this problem, we conduct growth experiments for a range of micro-organisms, including human fungal pathogens, baker's yeast, and common coliform bacteria, and uncover the following patterns. We observed that the maximal nutrient uptake rate and biomass yield were both decreasing functions of initial nutrient concentration. While a functional form for the relationship between biomass yield and initial nutrient concentration has been previously derived from first metabolic principles, here we also derive the form of the relationship between maximal nutrient uptake rate and initial nutrient concentration. Incorporating these two functions into a model of microbial growth allows for variable growth parameters and enables us to substantially improve predictions for microbial dynamics in a range of initial nutrient concentrations, compared to keeping growth parameters fixed.

AbstractUnderstanding how microbial traits affect the evolution and functioning of microbial communities is fundamental for improving the management of harmful microorganisms, while promoting those that are beneficial. Decades of evolutionary ecology research has focused on examining microbial cooperation, diversity, productivity and virulence but with one crucial limitation. The traits under consideration, such as public good production and resistance to antibiotics or predation, are often assumed to act in isolation. Yet, in reality, multiple traits frequently interact, which can lead to unexpected and undesired outcomes for the health of macroorganisms and ecosystem functioning. This is because many predictions generated in a single‐trait context aimed at promoting diversity, reducing virulence or controlling antibiotic resistance can fail for systems where multiple traits interact. Here, we provide a much needed discussion and synthesis of the most recent research to reveal the widespread and diverse nature of multi‐trait interactions and their consequences for predicting and controlling microbial community dynamics. Importantly, we argue that synthetic microbial communities and multi‐trait mathematical models are powerful tools for managing the beneficial and detrimental impacts of microbial communities, such that past mistakes, like those made regarding the stewardship of antimicrobials, are not repeated.

Microbes commonly deploy a risky strategy to acquire nutrients from their environment, involving the production of costly public goods that can be exploited by neighbouring individuals. Why engage in such a strategy when an exploitation-free alternative is readily available whereby public goods are kept private? We address this by examining metabolism of Saccharomyces cerevisiae in its native form and by creating a new three-strain synthetic community deploying different strategies of sucrose metabolism. Public-metabolizers digest resources externally, private-metabolizers internalize resources before digestion, and cheats avoid the metabolic costs of digestion but exploit external products generated by competitors. A combination of mathematical modelling and ecological experiments reveal that private-metabolizers invade and take over an otherwise stable community of public-metabolizers and cheats. However, owing to the reduced growth rate of private-metabolizers and population bottlenecks that are frequently associated with microbial communities, privatizing public goods can become unsustainable, leading to population decline.

Microbial cooperation drives ecological and epidemiological processes and is affected by the ecology and demography of populations. Population density influences the selection for cooperation, with spatial structure and the type of social dilemma, namely public-goods production or self-restraint, shaping the outcome. While existing theories predict that in spatially structured environments increasing population density can select either for or against cooperation, experimental studies with both public-goods production and self-restraint systems have only ever shown that increasing population density favours cheats. We suggest that the disparity between theory and empirical studies results from experimental procedures not capturing environmental conditions predicted by existing theories to influence the outcome. Our study resolves this issue and provides the first experimental evidence that high population density can favour cooperation in spatially structured environments for both self-restraint and public-goods production systems. Moreover, using a multi-trait mathematical model supported by laboratory experiments we extend this result to systems where the self-restraint and public-goods social dilemmas interact. We thus provide a systematic understanding of how the strength of interaction between the two social dilemmas and the degree of spatial structure within an environment affect selection for cooperation. These findings help to close the current gap between theory and experiments.

Existing theory, empirical, clinical and field research all predict that reducing the virulence of individuals within a pathogen population will reduce the overall virulence, rendering disease less severe. Here, we show that this seemingly successful disease management strategy can fail with devastating consequences for infected hosts. We deploy cooperation theory and a novel synthetic system involving the rice blast fungus Magnaporthe oryzae. In vivo infections of rice demonstrate that M. oryzae virulence is enhanced, quite paradoxically, when a public good mutant is present in a population of high-virulence pathogens. We reason that during infection, the fungus engages in multiple cooperative acts to exploit host resources. We establish a multi-trait cooperation model which suggests that the observed failure of the virulence reduction strategy is caused by the interference between different social traits. Multi-trait cooperative interactions are widespread, so we caution against the indiscriminant application of anti-virulence therapy as a disease-management strategy.