Evolutionary theory and human health

Abstract

The fundamentals of evolutionary biology enable integrating biology, medicine, and public health into one comprehensive framework. This approach improved our understanding of many human health topics, including antimicrobial resistance (AMR), ageing, cancer, microbiomes, or behavioural disorders. In this thesis, I use theory and tools from evolutionary biology to examine new questions about a range of human health topics. In the first part, I focus on using evolutionary approaches to inform public health interventions. In a context of limited time and resources, it is critical to identify where to focus our efforts to combat the public health consequences of AMR. In chapter 2, I use a combination of data analysis and modelling to show that various interventions deployed to improve front-line therapy lead to a larger reduction in mortality and morbidity than the same interventions deployed for last-line therapy. This challenges current practice, which prioritises research on last-line antimicrobial resistance. Non-pharmaceutical interventions (NPI) are effective to control epidemics, but broad adherence to guidance is key to their success yet challenging to achieve. This stems from social dilemmas: although these interventions create the best public health outcomes for society as a whole, they impose high costs for individuals, meaning that it might not be in their self-interest to comply. In chapter 3, combined game theory and epidemiological modelling to show how the underlying logic of different NPIs shape behavioural response and the consequences for epidemic control. Understanding this connection can help designing more robust interventions in future pandemics. In part two, I focus on microbes’ behaviour and their relationship with Human health. Microbes are engaged in complex social lives. This allows drawing on social evolution theory to understand how microbes behave and impact Human health. One central insight of this framework is that all else being equal, increased genetic relatedness among individuals should promote cooperation, owing to indirect fitness benefits (“Hamilton’s rule”). In chapter 4 developed computational approaches to quantify genetic relatedness and cooperation from sequencing data. I applied these methods to analyse a large diversity of Human gut microbes, and, using phylogenetic comparative analyses, I found support for Hamilton’s prediction. Variation in relatedness across the gut microbial diversity predicts variation in levels of cooperation. In chapter 5, I continue with computational approaches to assess the link between microbial cooperation their ability to cause disease with a comparative approach. I found that virulence factors are broadly enriched in cooperative genes, and cooperation predict which human-associated microbes are pathogenic. Yet, more cooperative pathogens do not consistently cause greater harm to Humans, highlighting the strong context-dependency of disease severity. In chapter 6, I derive a general model of a microbe’s effect on its host. It shows that species with higher relatedness will evolve to have a larger effect on their host, while transmission ecology affects the direction of this effect. I tested these predictions using case-control microbiome metagenomic data covering a range of diseases. I found that relatedness is positively associated with health, while transmission had no significant effect.Overall, chapters 3-6 depict the importance of microbial cooperation for Human health at broad taxonomic scales. This work demonstrates the predictive power of kin selection theory for natural microbial populations and the importance of microbes’ population genetic structure to advance a predictive understanding of how microbes cooperate in our gut to the benefit and cost of our health. Collectively, these results provide several practical insights for public health policy and open up new research perspectives. They also illustrate two important values of using an evolutionary perspective as a framework for public health intervention and biomedical research. First, the power of generality for public health policy insights. Second, the ability to uncover conflict between individual vs public interests in public health management.