Predicting evolutionary dynamics of multi-drug resistance
Lead Research Organisation:
University of Manchester
Department Name: School of Biological Sciences
Abstract
There is an urgent need to develop novel approaches to halt the evolution and spread of antimicrobial resistance. Combination therapies (multiple drugs given as a single prescription) are promising, both for preventing resistance and for optimising treatments for specific infections. However, recent experimental work has shown that combination therapies can select for multi-drug resistance in conditions experienced by natural microbial populations (e.g. temporally-varying drug concentrations and elevated mutation rates).
To establish whether combination therapies are a viable strategy, we need predictive models for how well drug combinations prevent resistance under real-world conditions where antibiotics are present (including infection and agriculture). A combined modelling and experimental approach is required because testing more than a handful of drugs is a considerable logistical challenge-5 antibiotics screened at 10 doses requires nearly 10 million growth assays, beyond the limits of high-throughput technologies. However, models need to account for the basic biology of microbial growth under temporally-varying antibiotic levels, which requires experimental measurement. Model predictions will be validated by experimentally exposing bacterial populations to the best and worst identified combinations to see if multi-drug resistance evolves. This work is crucial for establishing combination therapies as a viable solution to the antibiotic resistance crisis.
To establish whether combination therapies are a viable strategy, we need predictive models for how well drug combinations prevent resistance under real-world conditions where antibiotics are present (including infection and agriculture). A combined modelling and experimental approach is required because testing more than a handful of drugs is a considerable logistical challenge-5 antibiotics screened at 10 doses requires nearly 10 million growth assays, beyond the limits of high-throughput technologies. However, models need to account for the basic biology of microbial growth under temporally-varying antibiotic levels, which requires experimental measurement. Model predictions will be validated by experimentally exposing bacterial populations to the best and worst identified combinations to see if multi-drug resistance evolves. This work is crucial for establishing combination therapies as a viable solution to the antibiotic resistance crisis.
Technical Summary
The aim of this project will be to combine mathematical modelling and experimental approaches to understand the origin and evolution of multi-drug resistance evolution under biologically-realistic conditions. The programme of work will follow three lines of investigation.
I will first develop a stochastic model of multi-drug resistance evolution incorporating in-host pharmacokinetics and bacterial growth models. I will investigate factors leading to environments with sub-inhibitory antibiotic concentrations, such as differences in pharmacokinetic profiles, or as during the use of antibiotics as growth promoters in animals. This will be modelled by varying bacterial growth rates as a function of time. This will create predictions for the probability that multi-drug resistance can evolve during the course of combination therapy.
I will parametrise the model by developing a predictive framework for epistatic interactions between arbitrary combinations of resistance mutations, using high-throughput transcriptomics together with growth rate and competitive fitness assays. This will allow us to model the effects of multi-drug resistance on bacterial fitness, without needing to exhaustively screen all possible combinations.
Finally, predictions from the model will be tested by experimentally evolving E. coli populations in environments with multiple antibiotics. Combinations will be evaluated for their ability to reduce the likelihood of multi-drug resistance evolving.
I will first develop a stochastic model of multi-drug resistance evolution incorporating in-host pharmacokinetics and bacterial growth models. I will investigate factors leading to environments with sub-inhibitory antibiotic concentrations, such as differences in pharmacokinetic profiles, or as during the use of antibiotics as growth promoters in animals. This will be modelled by varying bacterial growth rates as a function of time. This will create predictions for the probability that multi-drug resistance can evolve during the course of combination therapy.
I will parametrise the model by developing a predictive framework for epistatic interactions between arbitrary combinations of resistance mutations, using high-throughput transcriptomics together with growth rate and competitive fitness assays. This will allow us to model the effects of multi-drug resistance on bacterial fitness, without needing to exhaustively screen all possible combinations.
Finally, predictions from the model will be tested by experimentally evolving E. coli populations in environments with multiple antibiotics. Combinations will be evaluated for their ability to reduce the likelihood of multi-drug resistance evolving.
Publications

Gifford DR
(2018)
Identifying and exploiting genes that potentiate the evolution of antibiotic resistance.
in Nature ecology & evolution

Gifford DR
(2018)
Environmental pleiotropy and demographic history direct adaptation under antibiotic selection.
in Heredity

Krašovec R
(2018)
Opposing effects of final population density and stress on Escherichia coli mutation rate.
in The ISME journal
Description | BBSRC DTP Research Experience Placement (undergraduate summer studentship) |
Amount | £2,500 (GBP) |
Organisation | University of Manchester |
Sector | Academic/University |
Country | United Kingdom |
Start | 06/2018 |
End | 09/2018 |
Description | Core Facilities Pump Priming |
Amount | £4,877 (GBP) |
Organisation | University of Manchester |
Sector | Academic/University |
Country | United Kingdom |
Start | 06/2018 |
End | 06/2018 |
Description | Wellcome Trust Institutional Strategic Support Fund "Tackling antimicrobial resistance by understanding evolutionary landscapes" |
Amount | £250,670 (GBP) |
Funding ID | 204796/Z/16/Z |
Organisation | University of Manchester |
Sector | Academic/University |
Country | United Kingdom |
Start | 07/2018 |
End | 07/2021 |
Title | Simulation software for antibiotic resistance evolution |
Description | C++ coded high-throughput simulation of antibiotic resistance evolution. |
Type Of Technology | Software |
Year Produced | 2018 |
Impact | This software is contributing to a manuscript currently in preparation for submission. It will be used in future publications on predicting antibiotic resistance evolution. The code will be available along with the first publication. |
Description | ICMS Workshop: Stochastic models of evolving populations: from bacteria to cancer |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | International meeting of mathematical modelling in the area of evolutionary biology, bridging research networks in e.g. antimicrobial resistance, infection, and cancer |
Year(s) Of Engagement Activity | 2018 |
URL | https://www.icms.org.uk/stochasticmodels.php |
Description | Manchester Molecular and Genomic Evolution Symposium 2018 |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Helped to organise a national one-day symposium including talks and posters on molecular and genome evolution, with a particular focus on Early Career Researcher contributions. |
Year(s) Of Engagement Activity | 2018 |
URL | https://manchestermage.wordpress.com/ |
Description | RESIST Antimicrobial resistance workshop |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Presented research (talk) with an international group of leading experts in clinical, experimental, and mathematical modelling approaches to antimicrobial resistance. |
Year(s) Of Engagement Activity | 2018 |
URL | https://amr.lshtm.ac.uk/2018/02/01/workshop-modelling-amr-resist/ |
Description | University of Manchester Community Festival |
Form Of Engagement Activity | Participation in an open day or visit at my research institution |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Public/other audiences |
Results and Impact | "Experimenting with Evolution" stand: open day event allowing hands-on with laboratory (non-pathogenic) antibiotic resistant bacteria and computer simulations of evolution in a video game-style interactive competition. Explaining evolutionary principles. |
Year(s) Of Engagement Activity | 2018 |