After studying a Bachelor’s degree at Manchester Metropolitan University, I became interested in evolutionary biology, leading me to pursue a Master’s degree in this subject at the University of Bath. Although most of my classes focussed on the evolution of large, complex organisms and their behaviours, as well as conservation methods for endangered animal species, I was given my first opportunity to do some microbiology-based research as part of my dissertation, which I completed in the lab of Dr Nick Waterfield. This work consisted of lab-based experiments, studying the synergistic effect of mixed bacterial infections on model invertebrate hosts, and provided my first real exposure to the microbial world.
Following this, I combined my new-found interest in microbes with my longer-held passion for evolution, by completing a PhD at Newcastle University in the lab of Professor Martin Embley. Up until this point, I am not sure that I had ever heard of Linux, or a command line before. This drastically changed when I was exposed to the amazing potential that large-scale data analysis provides for helping us to understand the natural world. My research made use of a range of cell culture, cell imaging, molecular and bioinformatic techniques, to investigate the evolution of hydrogenosomes (mitochondrial homologues that make hydrogen) in some anaerobic protists, found in a variety of naturally anoxic habitats.
Towards the end of my PhD, I was given the opportunity to move away from the UK for the first time, and did so, since I was mostly attracted by the exciting work that was being published by the Ettema-Lab (but also to escape the impending doom of Brexit!) Here my research will involve the investigation of prokaryotes, which live in similar anoxic environments to the protists that I studied during my PhD, but I will be trying to understand their biological processes on a wider community-scale. To do this I will combine novel cell isolation techniques with high-throughput sorting and cutting-edge sequencing technologies. The data this generates will facilitate metagenomic analysis, to look for evidence of interactions in natural environments, occurring between microbial consortia.