Building a genomics framework for Serratia marcescens - an emerging hospital superbug

Serratia marcescens is a Gram negative bacterium that is an emerging cause of healthcare-associated infections and is increasingly recognised as a reservoir of antibiotic resistance. Serratia can cause a wide variety of infectious syndromes including bloodstream infection, pneumonia, urinary tract infection and skin and soft tissue infections. Treatment options for this deadly pathogen are limited, in particular because of both intrinsic and acquired resistance to most classes of antibiotics. This makes understanding Serratia and stopping its spread an important priority. Despite these challenges, there is currently a paucity of data on Serratia. In particular there have been few analyses of population structure, limiting our ability to analyse relatedness of these bacteria and thus complicating efforts at understanding healthcare-associated spread. Little is known about the underlying diversity, epidemiology and evolution of this species. We therefore aim to use cutting edge genomic approaches integrating both short-read (Illumina) and long-read (Oxford Nanopore) technologies to conduct comparative genomic analyses of Serratia evolution and spread. The student will apply the latest genomics analyses to investigate the underlying genetic diversity of Serratia strains collected in Australia and globally. The student will explore the ancestral relationships among these strains and identify and compare antibiotic resistance and virulence genes. The ultimate aim will be to develop typing schemas for implementation in tracking Serratia outbreaks, which could have wide applicability given the rapid emergence of this pathogen. Upon completion of the project, there will be a genomics framework that will shape global responses to this superbug threat. The scope of the work can be refined to accommodate projects of varying length and is best suited for students interested in the application of computational biology approaches (including command-line programs) to analyse and interpret large datasets. Specific analysis approaches will include de novo genome assembly and annotation, reference-based variant detection, BLAST search and phylogenetics. Prior experience using the Unix operating system and the Python programming language is preferred but not essential.