Evolutionary mechanisms of pathogens – from commensality to virulence

About the Project

Clinically relevant pathogens evolve through a complex interplay of within-host evolution (including host and antibiotic selective pressure and interactions with other microorganisms), transmission dynamics, and metapopulation processes between hosts. This multifaceted system is yet to be fully understood. However, there are simulation tools, simplified theoretical models and inference methods alongside a wealth of publicly available datasets that allow to assess how these different evolutionary processes play out in real populations.

In this project, you will: i) collect datasets of paired commensal and invasive/disease strains from common databases such as PubMLST, NCBI and ENA; ii) use inference tools to estimate fitness effects of mutations, population size changes, and diverse modes of selection on carriage and invasive/disease strains of the same bacterial species; ii) use computer simulation tools to model different evolutionary scenarios and infer the model that best fits observed data evaluated in i).

This approach will allow you to assess several questions:

• Which evolutionary scenarios lead to a change from being a commensal strain to becoming virulent? Is this purely selection-driven or a chance product of genetic drift?
• Do new invasive strains often evolve via long-term within-host evolution epochs, e.g. within immunocompromised patients?
• Does the distribution of fitness of observed genetic variation differ between commensal and invasive strains? Are these distributions species- or population-specific?

By integrating large-scale genomic datasets with advanced evolutionary inference and simulation modelling, this project will elucidate the mechanisms driving the evolution of bacterial strains from commensal to invasive states, offering critical insights into pathogen emergence and informing strategies for surveillance and intervention

Techniques that will be undertaken during the project

This project provides an opportunity to learn and apply state-of the-art computational, population genetics and evolution, and machine learning approaches. Specifically, the candidate will perform computer simulation of bacterial genomes , use population genomics tools for estimating genetic variation summary statistics in bacterial datasets and machine learning techniques to fit evolutionary scenarios to real-world genomes that you will assemble and align via genomics techniques.

Funding Notes

This project is only available on a self funded basis or if you have your own sponsorship.