My research interests
The goal of my research is to employ an interdisciplinary approach to understand how human bacterial pathogens spread and evolve in response to clinical treatments, including vaccination and antibiotics. Specifically, I use a combination of computational genomics, bioinformatics, phylogenetic, statistics and network theory to study the speed of evolution and adaptation in gram-positive bacteria (like Streptococcus pneumoniae) and gram-negative bacteria (like Klebsiella pneumoniae or Escherichia coli). My research interests can be divided into three specific areas.
Measuring speed of evolution of bacterial antigens – major vaccine targets
I am interested in the evolutionary process shaping the diversity of bacterial capsular
polysaccharides – one of the most diverse bacterial structures. Recently, I have shown that the capsular polysaccharide in Streptococcus pneumoniae – target of polysaccharide conjugate vaccine – is an evolutionary hotspot, with a potential to generate novel serotypes by recombination in the future, and that these serotypes could be potentially selected for by the vaccine. I am currently investigating a similar process in gram-negative enteric bacteria, including (but not limited to) Klebsiella pneumoniae, Escherichia coli and Salmonella enterica.
Improving fundamental understanding about transformation in recombinogenic bacteria
Some bacterial pathogens, like Streptococcus pneumoniae, are highly transformable, namely have a strong potential to acquire foreign DNA from other bacterial cells. My research focuses on questions related to a better understanding of this process using recent advances in genomics, and understanding its implications for epidemiology of these bacteria.For example, I demonstrated that in S. pneumoniae undergoes two mechanisms of recombination: micro-recombination and macro-recombination, the latter of which is the main evolutionary driver. My work in collaboration with Nick Croucher (Imperial College) and Christophe Fraser (Oxford) provided fundamental insight into the benefits of transformation in S. pneumoniae, and thus why it possibly evolved. Currently, I am working on improving our understanding of whether higher rates of transformation are a good predictor of resistance toantibiotics in S. pneumoniae.
Generating new methods to infer population structure of large data collections
The falling price of genetic sequencing is leading to the generation of big genome data collections. Such collections can be challenging to analyse, and there is an increasing need to introduce novel methods for their analysis. One of the goals of my research is to collaborate with statisticians and computer scientists to develop methods for inference of the population genetic structure of large collections of bacterial genomes.In collaboration with Dr. Pekka Marttinen (Aalto University), I developed a method for the inference of mosaicism and recombination in large and diverse collections of bacterial genomes. Currently, I am working in collaboration with Dr Leonid Chindelevitch (Simon Fraser University) and Dr Timo Smieszek (Public Health England) to employ novel alignment-free methods and network theory to rapidly infer the population genetic structure in collections of tens of thousands bacterial genomes, which will start emerging in the near future.