Primary Supervisor: Professor Alan McNally, Institute of Microbiology and Infection
Secondary supervisor: Dr Nicole Wheeler
PhD project title: How does microbial metabolism influence the evolution of Antimicrobial Resistance
University of Registration: University of Birmingham
Multi-drug resistant (MDR) Gram negative pathogens represent one of the most significant threats to global public health. MDR clones of Escherichia coli and Klebsiella pneumoniae now dominate the global landscape of urinary tract infections and bloodstream infections. Our group has shown that in the MDR E. coli ST131 lineage, there are signatures of selection in genes involved in metabolism that are absolutely unique and specific to the MDR clone. We have also reported such MDR clone-specific alleles of metabolism genes in other E. coli lineages containing MDR clones such as ST167, ST648 and ST410. Unpublished data from our group sought to determine how robust this observation is by analysing a data set of 30k E. coli genomes representing the full phylogenetic diversity of the species. Our data shows that there is clear correlation between the number of AMR genes present in a genome and the level of allelic diversity present in metabolism genes, clearly showing a link between MDR and metabolic sequence divergence. Our hypothesis is that selection on genes involved in core metabolic pathways underpin the evolution, emergence and expansion of pandemic MDR clones of E. coli.
Understanding the evolutionary processes and mechanisms by which bacterial pathogens emerge is a key challenge for evolutionary biology and microbiology. MDR E. coli cause a high burden of invasive disease in animals and humans that is increasingly difficult to treat. Their emergence is driven by a small number of pandemic clones which carry MDR plasmids, and contain string signatures of specific selection in core metabolism genes. We also know that MDR plasmids affect core metabolism gene expression in a consistent and lineage independent manner. We hypothesise that selection on core metabolic pathways is a driving event in the formation of pandemic clones, enhancing their fitness and potentiating resistance leading to MDR plasmid acquisition and MDR pandemic clones.
Specific objectives of this research project are to determine:
- What are the common patterns of mutations and/or gene gains that underpin the evolution of MDR clones?
- How do mutations in core metabolism genes potentiate the formation of MDR clones?
In Objective 1 we will use our collated data set of 30k E. coli genomes to perform a population structure controlled GWAS analysis, identifying every gene gain/loss and mutation event associated with being an MDR clone across 20 of the most common E. coli lineages.
In Objective 2 we will use Machine Learning and AI approaches to identify which of these genetic events significantly predicts an MDR clone, and then confirm these by performing experimental evolution of different lineages in the presence of low-level antibiotics to determine which mutation events give rise to resistance.
- Dynamics of intestinal multidrug-resistant bacteria colonisation contracted by visitors to a high-endemic setting: a prospective, daily, real-time sampling study. Kantele A, Kuenzli E, Dunn SJ, Dance DAB, Newton PN, Davong V, Mero S, Pakkanen SH, Neumayr A, Hatz C, Snaith A, Kallonen T, Corander J, McNally A. Lancet Microbe. 2021 Apr;2(4):e151-e158.
- The evolution and transmission of multi-drug resistant Escherichia coli and Klebsiella pneumoniae: the complexity of clones and plasmids. Dunn SJ, Connor C, McNally A. Curr Opin Microbiol. 2019 Oct;51:51-56.
- Diversification of Colonization Factors in a Multidrug-Resistant Escherichia coli Lineage Evolving under Negative Frequency-Dependent Selection. McNally A, Kallonen T, Connor C, Abudahab K, Aanensen DM, Horner C, Peacock SJ, Parkhill J, Croucher NJ, Corander J. mBio. 2019 Apr 23;10(2):e00644-19.
BBSRC Strategic Research Priority: Understanding the Rules of Life: Microbiology
Techniques that will be undertaken during the project:
Microbial genomic analysis and Microbial GWAS; Machine Learning and phenotype prediction in bacteria, bacterial experimental evolution.
Contact: Professor Alan McNally, University of Birmingham