Why do immune responses to infectious diseases depend on age? We have seen during the COVID-19 pandemic that older people are more at risk of severe disease, but for other diseases, children may be more at risk. Sometimes this can be explained by a person’s prior exposure to disease, as our immune systems are able to remember previous infections—this is how vaccines work. However, in many human, animal or plant diseases, age still affects immunity even after accounting for prior exposure. A possible explanation is that organisms have evolved different levels of resistance to pathogens at different ages. Looking at how disease resistance evolves can help with understanding how infectious diseases affect humans and other species.
Simon Fraser University (SFU) mathematics professor Ben Ashby studies the evolution of infectious diseases and in turn how they affect the evolution of host immunity. He is the principal investigator of the Eco-Evo Theory Group at SFU, part of the Mathematics, Genomics and Prediction in Infection and Evolution—MAGPIE—research group.
Ashby and his research team use mathematical models and simulations to understand how infectious diseases evolve to become less or more contagious or deadly. They also look at how hosts evolve to defend themselves against infection. Together, the research provides evolutionary insights into how resistance and immunity change over time, and why immunity varies with age.
From an evolutionary perspective, it might appear to make sense for disease resistance to be stronger in juveniles otherwise they might not reach maturity to reproduce, but this is not always the case. Organisms cannot necessarily be good at everything, and being resistant may come at a cost. For instance, resisting infection might mean less resources for growth in juveniles or less for reproduction in adults. It may be better to risk infection when young if it frees up resources for growth or future reproduction. One way to understand how these trade-offs between resistance and other host traits affect evolution is through mathematical modelling.
Ashby’s research article, The evolution of age-specific resistance to infectious disease, with Emily Bruns from the University of Maryland and Lydia Buckingham from the University of Bath, was published in Proceedings B, the Royal Society’s biological research journal. It builds on previous mathematical models to explore how a range of trade-offs with different host characteristics affect the evolution of resistance across the host lifespan.
We spoke with Professor Ashby about his research.
What did the mathematical model you designed seek to understand?
From an evolutionary perspective, it can seem puzzling that younger individuals are often less resistant to infection than older individuals, even after accounting for prior exposure to pathogens. If young individuals don’t reach maturity, then they won’t produce any offspring, which is very bad from an evolutionary point of view. We have previously found that sometimes evolution leads to younger individuals being more prone to infection than adults, because resistance to infection may come at a cost to an organism’s growth rate or the number of offspring they have later in life. In this study, we wanted to understand how different types of costs affect the evolution of resistance at different stages of life.
What were the results and how do they provide insights into why organisms have evolved to be more or less resistant to infectious diseases at different ages?
Our modelling reveals that different balances between conflicting needs can determine whether adults or juveniles are better at resisting infection. For instance, when organisms can either defend against infection when they are young or have more offspring later in life, adults are usually more resistant than juveniles. But when resistant individuals instead mature slower, adults may be more resistant than juveniles. This is because producing fewer offspring later in life is much more costly than simply maturing a little slower, and so evolution leads to different levels of resistance at different life stages.
What are the applications of this research, for example could it add to the understanding of human diseases?
This research tells us about how evolution balances protection against infection at different ages and other characteristics such as how fast organisms grow or how many offspring they produce. There are certain human diseases where children have weaker innate immune responses than adults, but we don’t know the reason why. This type of modelling gives us potential clues from an evolutionary perspective. It also helps to inform artificial selection of food crops, where seedlings and full-grown plants can have different levels of resistance, but this may result in smaller plants or fewer seeds.
How did you become a mathematician—and what advice do you have for aspiring scientists in this field?
I have always been fascinated by how the complexity of the real world can often be distilled into relatively simple mathematical models that still give profound insights about nature. As an undergraduate, I learned how mathematics can be used to study biological systems, and this sparked my interest in modelling infectious diseases and later, evolution. My advice to any aspiring scientists in mathematical biology is to work with biologists where possible, so that models don’t become detached from the real world.
To learn more about Ashby’s research visit The Eco-Evo Theory Group.
To view the source code for the mathematical model described visit this link.
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