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Furthering our understanding of microbial community dynamics through antimicrobial-driven dysbiosis

Project Description

The identification of antimicrobial resistance (AMR) genes from metagenomic data has vastly increased our knowledge of their prevalence and communicability between and within microbiomes. However, in vitro confirmation of their activity and the effect of their action on microbial community dynamics has been hampered by the absence of the vast majority of the AMR harbouring organisms in culture collections. This project will address this gap in our knowledge by using bioinformatically generated AMR profiles of uncultured organisms to lead targeted culturing to isolate these organisms. The project will benefit from samples and data already generated as part of the EU MASTER project of which the PI and Co-I are work-package leaders. This hypothesis-driven project will generate novel metagenomically-assembled genomes from samples preserved in glycerol, allowing the in silico identification of the AMR profiles of previously uncultured organisms. This will allow the generation of targeted culture conditions bioinformatically predicted to enrich for those previously uncultured organisms. These novel cultured single or interacting communities of organisms will allow the testing of their presence on the dynamics of the microbial community during critical stages of establishment and transition in the microbiome. The student will join the Creevey lab ( in the School of Biological Sciences/Institute for Global Food Security and will gain experience in computational biology approaches for understanding microbial ecosystems.

This project will be supervised by Professor Chris Creevey and Professor Sharon Huws of Queen’s University School of Biological Sciences.

Start Date: 1 October 2020
Duration: 3.5 years

Funding Notes

The studentship will be funded by the Department for the Economy (DfE). Please read the full information on eligibility criteria: View Website

However, there may be flexibility to fund a small number of exceptional International applicants.

How good is research at Queen’s University Belfast in Agriculture, Veterinary and Food Science?

FTE Category A staff submitted: 33.40

Research output data provided by the Research Excellence Framework (REF)

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