PhD Studentship: CENTA 2027-B05 Novel ways to deal with the complexities of detecting selection for antimicrobial resistance under realistic natural conditions

Antimicrobial resistance (AMR) is a major public health threat. The environment plays an important role as resistant microorganisms are transmitted between people and animals through the environment; they also survive or thrive there and exchange genes.

One of the big debates is whether the low concentrations of antibiotics in the environment are selecting for resistance.

One approach to experimentally test this is to compete a ‘special pair’ of sensitive and resistant bacteria at a range of antibiotic concentrations in the lab, where their only difference is resistance so they share the same ecological niche. This is an accurate and sensitive method, but the conditions in the lab do not mimic the environment.

An alternative approach is to bring a complex sample from the environment into the lab and measure whether resistance genes are increasing at different antibiotic concentrations. This would seem to mimic the environment as a diverse community of bacteria is used, but the inevitable shifts in microbial community composition when incubating in rich media can result in increased resistance levels in the absence of selection.

We have a different idea, to use mathematical models based on results from simple lab experiments and then create the complexity in the model by including the effects of diversity, predation, low substrate concentrations etc, ultimately predicting at which concentration selection happens in the environment and also understanding the effects of the complications. This will be coupled with experiments under natural conditions to test and validate the model, in flume mesocosms that can mimic river conditions in the lab at UKCEH Wallingford and also in situ in rivers. For this, we will put the special pair in ‘cages’ (dialysis bags) so they cannot escape but are immersed in the environment and experience the same nutrient concentrations etc. as the other bacteria in the surrounding environment.

Such a validated mathematical model that can predict the risk of selection under environmental conditions is hugely important because it is impossible to expose thousands of different pathogenic and environmental bacteria with various (combinations) of resistances to many levels of hundreds of potentially selective antibiotics and other compounds under a variety of relevant environmental conditions.

For further information on this project and details of how to apply to it please click the 'Apply' button above.

Further information on how to apply for a CENTA studentship can be found on the CENTA website: https://centa.ac.uk/apply/

References:

This project is offered through the CENTA3 DLA, funded by the Natural Environment Research Council (NERC). Funding covers: annual stipend, tuition fees (at home-fee level), Research Training Support Grant.

Academic requirements: at least a 2:1 at UK BSc level or a pass at UK MSc level or equivalent.

Further information: https://centa.ac.uk/apply/

International students are eligible for studentships to a maximum of 30% of the cohort. Funding does not cover any additional costs relating to moving or residing in the UK. International applicants must fulfil the University of Birmingham’s international student entry requirements, including English language.  Further information: https://www.birmingham.ac.uk/postgraduate/pgt/requirements-pgt/international/index.aspx.

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