BACKGROUND The threat of antimicrobial resistant pathogens is a growing and present danger in modern life. The Klebsiella pneumoniae species complex is a group of several closely-related opportunistic bacterial pathogens, which are a serious threat to public health due to increasing levels of virulence and antimicrobial resistance. This species complex comprises Klebsiella pneumoniae, Klebsiella variicola, Klebsiella quasipneumoniae and Klebsiella africana, each with varying nutrient and metabolic requirements. While these are separate species, occasionally we can detect ‘hybrid’ isolates that contain pieces of chromosome from two different species, generated via a process known as chromosomal recombination. Sometimes these recombinations make up large chunks of the isolate’s genome, in some cases up to 20%! It is unclear what is being lost and what is being gained through these large chromosomal interspecies recombination events. In this project the student will study the impact of these events by simulating the nutrient requirements and metabolic capabilities of Klebsiella species hybrids, and comparing them to non-hybrid isolates. AIMS 1. Identify Klebsiella hybrid genomes from our curated Klebsiella dataset, and characterise the underlying chromosomal recombination events 2. Generate genome-scale metabolic models for all hybrid genomes, plus representative isolates from non-hybrid species 3. Simulate growth phenotype predictions in silico and identify phenotypes that are conserved / differ between species hybrid and their species parents WHAT YOU WILL LEARN • How to drive and answer research questions • Computational biology analysis pipelines • How to manage and analyse medium-sized datasets (~100 bacterial genomes) • Command line computing, Python and R experience • Effective communication of a complex topic • How to create information-rich, clean scientific figures YOUR SKILLS AND EXPERIENCE Required • Background in: o Microbiology OR o Genetics OR o Evolution • Interests in: o Computational biology/bioinformatics o Evolution and microbial genetics o Metabolism and metabolic pathway analysis Ideal • Experience with: o Command line/unix environments AND/OR o Python programming language AND/OR o R programming language SUPERVISION You will be co-supervised by Dr. Kelly Wyres and Dr. Ben Vezina in the microbial genomics lab at Monash University’s Department of Infectious Diseases (Alfred Hospital campus), and will have the opportunity to interact with other graduate students and post-doctoral research fellows in the team. See https://holtlab.net/ for more information.
infectious disease, Klebsiella, antibiotic resistance, genomics, metabolism, computational biology, microbiology, molecular biology, biochemistry
Masters by coursework
Alfred Research Alliance