Project highlights

  • Big picture AMR dynamics in the environment from sewage to rivers
  • Cross-disciplinary skill set
  • Communication with stakeholders and public outreach welcome

Overview

The widespread use of antibiotics in humans and animals has not only selected for resistance genes but also for plasmids carrying them. This has led to a growing global concern for the spread of antimicrobial resistance (AMR), which would have enormous public health implications for nations of all income and development levels. This project would enable the student to gain inter-disciplinary training and mentoring to tackle a largely unknown but critical link in the spread of AMR, namely if and to what extent AMR can persist in our river networks. The student would have a range of options in river systems to explore, from new collaborations in India to the Thames in the UK, this project will provide the first assessment of how and when AMR can enter, persist, and be transported, within river systems with different pollution sources.

Resistance genes can enter the environment via hotspots such as hospital sewers, wastewater treatment plants and animal manures and slurries on farms. Runoff from these fields and effluent from wastewater treatment enter rivers and interact with river sediments. In addition, large rainfall and runoff events can overwhelm storm drainage and wastewater treatment systems, allowing untreated sewage to enter river systems directly.

Building on previous work, this project will develop a mechanistic mathematical model to better understand resistance selection and transport from wastewater, wastewater treatment to where effluent enters the river network, within the river itself, and its downstream transport. Importantly, this model will be placed in context of the hydrological functioning of the Thames catchment, and also incorporate the large metagenomic sequencing and antibiotic concentration datasets available throughout the catchment. Depending on the background and interests of the student, experimental studies of selection under low concentrations of antibiotics to validate the mathematical model can be included.

CENTA Flagship

This is a CENTA Flagship Project

Case funding

This project is suitable for CASE funding

Host

University of Birmingham

Theme

  • Climate and Environmental Sustainability
  • Organisms and Ecosystems

Supervisors

Project investigator

  • Jan-Ulrich Kreft (University of Birmingham)

 

Co-investigators

  • Joshua Larsen (University of Birmingham)
  • Prof Elizabeth Wellington (University of Warwick)
  • Dr Chris Quince (University of Warwick)
  • Dr Andrew Singer (CEH) and Dr Wiebke Schmidt (EA)

How to apply

Methodology

The mathematical model will be based on ordinary differential equations and potentially include an agent-based sub-model to capture the nested organization of resistance genes on plasmids in host bacteria. We are using this approach to extend the Activated Sludge Model 1 with resistance plasmid transmission, antibiotic turnover and including enteric bacteria from faeces to model exchange of plasmids between enteric and indigenous wastewater bacteria. We are currently analysing the one compartment model (manuscript in preparation). In the project, we will develop the multi-compartment model and use Approximate Bayesian Computation (ABC) statistical model selection and inference methodology. Experimental studies of selection at low concentrations of antibiotics mimic conditions in the environment can be included.

Training and skills

This project is highly interdisciplinary and will provide the student with a unique quantitative skills set including mathematical modelling, statistical data analysis and programming and potentially laboratory skills.

This will be enhanced by interdisciplinary collaboration with the Larsen, Wellington, Singer and Quince groups and our collaborators in India, as the DR will learn to communicate and collaborate with scientists from many disciplines. The modelling will also guide future experimental effort by identifying the most important parameters and processes.

Moreover, the DR will have the opportunity for public outreach activities to inform the AMR debate with the results of our project. Project management and communication skills will also be gained.

Partners and collaboration

This project provides a unique opportunity to work closely with a number of collaborators across different disciplines. Firstly, at the University of Birmingham with Jan Kreft, whose group has >15 years’ experience in mathematical modelling, including modelling plasmid dynamics and AMR dynamics. Secondly, with Josh Larsen, whose group has extensive experience in catchment hydrological processes, monitoring, and modelling.

At the University of Warwick, the Wellington lab has for many years driven forward the research on AMR dynamics in the Thames catchment with collaborators from CEH, Thames Water and others.

The Quince group is at the forefront of developing more rigorous statistical and bioinformatic algorithms e.g. for reconstructing genomes from metagenomes.

Further details

Applications need to be submitted via the University of Birmingham postgraduate portal, https://sits.bham.ac.uk/lpages/LES068.htm, by midnight 11.01.2021. Please first check whether the primary supervisor is within Geography, Earth and Environmental Sciences, or in Biosciences, and click on the corresponding PhD program on the application page.

This application should include

  • a brief cover letter, CV, and the contact details for at least two referees
  • a CENTA application form
  • the supervisor and title of the project you are applying for under the Research Information section of the application form.

Referee’s will be invited to submit their references once you submit your application, but we strongly encourage applicants to ensure referees are aware of your submission and expecting a reference request from us. Students are also encouraged to visit and explore the additional information available on the CENTA website.


This is a CENTA Flagship Project

These have been selected because the project meets specific characteristics such as CASE support, collaboration with our CENTA high-level end-users, diversity of the supervisory team, career development of the supervisory team, collaboration with one of our Research Centre Partners (BGS, CEH, NCEO, NCAS) or student co-designed project. These characteristics are a CENTA priority. Studentships associated with Flagship projects will be provided exactly the same level of support as all other studentships.

Possible timeline

Year 1

Learning to build and building a multi-compartment AMR dynamics and transport model (Paper 1). This will extend an existing single-compartment model to include upstream and downstream compartments to create a complete model of AMR transmission on the scale of a catchment. Learn about and start using published data and data generated by the JPIAMR and the Thames catchment project, in collaboration with another CENTA DR, James Delaney, supervised by Wellington & Quince, who is working with this dataset.

Year 2

Develop Bayesian model selection and parameter inference methods to fit the mechanistic model built in year 1 to the datasets mentioned above (Paper 2). We aim to develop a robust statistical pipeline for selection of mechanistic mathematical models describing the underlying processes in AMR transmission rather than just fitting a statistical model to the outcome of these processes.

Year 3

Simulate the effect of various mitigation strategies on AMR transmission (Paper 3).

Based on the model and its inferred parameters, quantify the risk of resistance transmission in different compartments and overall using Bayesian Networks (Paper 4).

Further reading

Hassoun-Kheir N, Stabholtz Y, Kreft JU, de la Cruz R, Romalde JL, Nesme J, Sørensen SJ, Smets BF, Graham D, Paul M (2020). Comparison of antibiotic-resistant bacteria and antibiotic resistance genes abundance in hospital and community wastewater: A systematic review. Science of The Total Environment 743: 140804

Arya S, Todman H, Baker M, Hooton S, Millard A, Kreft JU, Hobman JL, Stekel DJ (2020). A generalised model for generalised transduction: the importance of co-evolution and stochasticity in phage mediated antimicrobial resistance transfer. FEMS Microbiology Ecology 96: fiaa100

Hellweger FL, Clegg RJ, Clark JR, Plugge CM, Kreft JU (2016). Advancing microbial sciences by individual-based modelling. Nature Reviews Microbiology 14: 461–471

Amos GCA, Hawkey PM, Gaze WH, Wellington EM (2014). Waste water effluent contributes to the dissemination of CTX-M-15 in the natural environment. The Journal of Antimicrobial Chemotherapy 69: 1785–1791

Amos GCA, Zhang L, Hawkey PM, Gaze WH, Wellington EM (2014). Functional metagenomic analysis reveals rivers are a reservoir for diverse antibiotic resistance genes. Veterinary Microbiology 171: 441–447

Amos GCA, Gozzard E, Carter CE, Mead A, Bowes MJ, Hawkey PM, Zhang L, Singer AC, Gaze WH, Wellington EMH (2015). Validated predictive modelling of the environmental resistome. ISME Journal 9: 1467–1476

Lehmann K, Bell T, Bowes MJ, Amos GCA, Gaze WH, Wellington EMH, Singer AC (2016). Trace levels of sewage effluent are sufficient to increase class 1 integron prevalence in freshwater biofilms without changing the core community. Water Research 106: 163–170

Singer AC, Shaw H, Rhodes V, Hart A (2016). Review of Antimicrobial Resistance in the Environment and Its Relevance to Environmental Regulators. Frontiers in Microbiology 7: 1728

 

COVID-19

The project is based on mathematical modelling and may be combined with experimental studies of selection of antimicrobial resistance. The focus can shift more towards the data analysis and mathematical modelling side if lab closures due to COVID make this necessary. There is also considerable flexibility regarding the timeline, allowing us to bring experimental work forward or push it back as we start the project with substantial data being available already.