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
Krašovec R
(2017)
Spontaneous mutation rate is a plastic trait associated with population density across domains of life.
in PLoS biology
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
Gifford DR
(2018)
Identifying and exploiting genes that potentiate the evolution of antibiotic resistance.
in Nature ecology & evolution
Krašovec R
(2019)
Measuring Microbial Mutation Rates with the Fluctuation Assay.
in Journal of visualized experiments : JoVE
Knight GM
(2019)
Mathematical modelling for antibiotic resistance control policy: do we know enough?
in BMC infectious diseases
Gifford D
(2019)
Mutators drive evolution of multi-resistance to antibiotics
Gifford DR
(2019)
Life on the frontline reveals constraints.
in Nature ecology & evolution
BerrÃos-Caro E
(2021)
Competition delays multi-drug resistance evolution during combination therapy.
in Journal of theoretical biology
Shahabuddin A
(2023)
Coincidence analysis of efflux pumps in the Escherichia coli pangenome
Gifford DR
(2023)
Mutators can drive the evolution of multi-resistance to antibiotics.
in PLoS genetics
Gifford DR
(2024)
Environmental and genetic influence on the rate and spectrum of spontaneous mutations in Escherichia coli.
in Microbiology (Reading, England)
Jago MJ
(2025)
High-throughput method characterizes hundreds of previously unknown antibiotic resistance mutations.
in Nature communications
| Description | BBSRC DTP Research Experience Placement (undergraduate summer studentship) |
| Amount | £2,500 (GBP) |
| Organisation | University of Manchester |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 05/2018 |
| End | 09/2018 |
| Description | Core Facilities Pump Priming |
| Amount | £4,877 (GBP) |
| Organisation | University of Manchester |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 05/2018 |
| End | 06/2018 |
| Description | Costs of fluoroquinolone resistance in clinical E. coli: a potential explanation for similarities in resistance between the UK and Canada |
| Amount | £10,131 (GBP) |
| Funding ID | NE/T014709/1 |
| Organisation | Natural Environment Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 03/2020 |
| End | 03/2021 |
| Description | Determining the architecture of antibiotic resistance evolvability |
| Amount | £577,932 (GBP) |
| Funding ID | BB/X007979/1 |
| Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 05/2023 |
| End | 03/2026 |
| Description | Life on the 'mild' side: adaptation of an extremophile archaeon to a mesophilic lifestyle |
| Amount | £80,613 (GBP) |
| Funding ID | NE/X012662/1 |
| Organisation | Natural Environment Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 02/2023 |
| End | 01/2024 |
| Description | Manchester-Toronto Alliance for Microbial Eco-evolutionary Dynamics (part of UKRI-BBSRC International Institutional Partnership Funding Opportunity) |
| Amount | £20,000 (GBP) |
| Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 03/2024 |
| End | 06/2024 |
| Description | Microbial population diversity as a driver of antibiotic resistance evolution |
| Amount | £98,768 (GBP) |
| Funding ID | 2282521 |
| Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2019 |
| End | 09/2023 |
| Description | Suppressing mutation-mediated resistance through antibiotic combination treatment |
| Amount | £99,916 (GBP) |
| Organisation | Springboard |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 03/2022 |
| End | 02/2024 |
| 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 | 06/2018 |
| End | 07/2021 |
| Title | Data from: Environmental and genetic influence on rate and spectrum of spontaneous mutations in Escherichia coli |
| Description | Supplementary Material for 'Environmental and genetic influence on the rate and spectrum of spontaneous mutations in Escherichia coli ', as described in Microbiology. Spontaneous mutations are the ultimate source of novel genetic variation on which evolution operates. Although mutation rate is often discussed as a single parameter in evolution, it comprises multiple distinct types of changes at the level of DNA. Moreover, the rates of these distinct changes can be independently influenced by genomic background and environmental conditions. Using fluctuation tests, we characterised the spectrum of spontaneous mutations in Escherichia coli grown in low and high glucose environments. These conditions are known to affect the rate of spontaneous mutation in wild-type MG1655, but not in a ?luxS deletant strain-a gene with roles in both quorum sensing and the recycling of methylation products used in Escherichia coli's DNA repair process. We find an increase in AT>GC transitions in the low glucose environment, suggesting that processes relating to the production or repair of this mutation could drive the response of overall mutation rate to glucose concentration. Interestingly, this increase in AT>GC transitions is maintained by the glucose non-responsive ?luxS deletant. Instead, an elevated rate of GC>TA transversions, more common in a high glucose environment, leads to a net non-responsiveness of overall mutation rate for this strain. Our results show how relatively subtle changes, such as the concentration of a carbon substrate or loss of a regulatory gene, can substantially influence the amount and nature of genetic variation available to selection. Strains are available upon request. File S1, available in the online version of this article, contains the mutations found in sequenced rifampicin-resistant strains originating from the fluctuation test and used to assess changes in mutational spectrum. File S2 contains the R analysis code used to perform all statistical analyses and generate figures. File S3 contains mutant counts used to estimate mutation rates to rifampicin resistance for MG1655 and ?luxS strains grown at low and high glucose in the fluctuation test. File S4 contains population density data (Nt) for MG1655 and ?luxS strains grown at low and high glucose in the fluctuation test. Supplemental Methods |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| URL | https://microbiology.figshare.com/articles/dataset/Data_from_Environmental_and_genetic_influence_on_... |
| Title | Data from: Environmental and genetic influence on the rate and spectrum of spontaneous mutations in Escherichia coli |
| Description | Abstract: Spontaneous mutations are the ultimate source of novel genetic variation on which evolution operates. Although mutation rate is often discussed as a single parameter in evolution, it comprises multiple distinct types of changes at the level of DNA. Moreover, the rates of these distinct changes can be independently influenced by genomic background and environmental conditions. Using fluctuation tests, we characterised the spectrum of spontaneous mutations in Escherichia coli grown in low and high glucose environments. These conditions are known to affect the rate of spontaneous mutation in wild-type MG1655, but not in a ?luxS deletant strain-a gene with roles in both quorum sensing and the recycling of methylation products used in Escherichia coli's DNA repair process. We find an increase in AT>GC transitions in the low glucose environment, suggesting that processes relating to the production or repair of this mutation could drive the response of overall mutation rate to glucose concentration. Interestingly, this increase in AT>GC transitions is maintained by the glucose non-responsive ?luxS deletant. Instead, an elevated rate of GC>TA transversions, more common in a high glucose environment, leads to a net non-responsiveness of overall mutation rate for this strain. Our results show how relatively subtle changes, such as the concentration of a carbon substrate or loss of a regulatory gene, can substantially influence the amount and nature of genetic variation available to selection.File S1 contains the mutations found in sequenced rifampicin-resistant strains originating from fluctuation test and used to assess changes in mutational spectrum.File S2 contains the R analysis code used to perform all statistical analyses and generate figures.File S3 contains mutant counts used to estimate mutation rates to rifampicin resistance for MG1655 and ?luxS strains grown at low and high glucose in the fluctuation test.File S4 contains population density data (Nt) for MG1655 and ?luxS strains grown at low and high glucose in the fluctuation test. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| URL | https://figshare.manchester.ac.uk/articles/dataset/Data_from_Environmental_and_genetic_influence_on_... |
| Title | Data from: Environmental and genetic influence on the rate and spectrum of spontaneous mutations in Escherichia coli |
| Description | Abstract: Spontaneous mutations are the ultimate source of novel genetic variation on which evolution operates. Although mutation rate is often discussed as a single parameter in evolution, it comprises multiple distinct types of changes at the level of DNA. Moreover, the rates of these distinct changes can be independently influenced by genomic background and environmental conditions. Using fluctuation tests, we characterised the spectrum of spontaneous mutations in Escherichia coli grown in low and high glucose environments. These conditions are known to affect the rate of spontaneous mutation in wild-type MG1655, but not in a ?luxS deletant strain-a gene with roles in both quorum sensing and the recycling of methylation products used in Escherichia coli's DNA repair process. We find an increase in AT>GC transitions in the low glucose environment, suggesting that processes relating to the production or repair of this mutation could drive the response of overall mutation rate to glucose concentration. Interestingly, this increase in AT>GC transitions is maintained by the glucose non-responsive ?luxS deletant. Instead, an elevated rate of GC>TA transversions, more common in a high glucose environment, leads to a net non-responsiveness of overall mutation rate for this strain. Our results show how relatively subtle changes, such as the concentration of a carbon substrate or loss of a regulatory gene, can substantially influence the amount and nature of genetic variation available to selection.File S1 contains the mutations found in sequenced rifampicin-resistant strains originating from fluctuation test and used to assess changes in mutational spectrum.File S2 contains the R analysis code used to perform all statistical analyses and generate figures.File S3 contains mutant counts used to estimate mutation rates to rifampicin resistance for MG1655 and ?luxS strains grown at low and high glucose in the fluctuation test.File S4 contains population density data (Nt) for MG1655 and ?luxS strains grown at low and high glucose in the fluctuation test. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| URL | https://figshare.manchester.ac.uk/articles/dataset/Data_from_Environmental_and_genetic_influence_on_... |
| Description | AMR Modelling Group |
| Organisation | University of Manchester |
| Department | School of Physics and Astronomy Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Idea generation for a model that predicts when antimicrobial resistance will evolve under different environmental conditions. Experimental tests of mathematical modellers. |
| Collaborator Contribution | Developing mathematical models and simulations of antimicrobial resistance evolution under varying environments. |
| Impact | DR Gifford, E Berríos-Caro*, C Joerres*, T Galla, CG Knight (2019) Mutators drive evolution of multi-resistance to antibiotics, bioRxiv, doi: 10.1101/643585. |
| Start Year | 2019 |
| Description | Collaboration with Dr Alex Wong (Carleton University, Canada) |
| Organisation | Carleton University |
| Country | Canada |
| Sector | Academic/University |
| PI Contribution | Development of research plan. |
| Collaborator Contribution | Development of research plan. Offer to train research student in specialist technique. Waived bench fees and tuition for research exchange student. |
| Impact | Outputs have not yet been generated as the actual exchange has been delayed due to COVID-19 travel restrictions. |
| Start Year | 2019 |
| 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 | Manchester Women in Science (part of 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 | Local |
| Primary Audience | Public/other audiences |
| Results and Impact | Engaged in discussions with the general public about important research advances made by women with an association to Manchester. |
| Year(s) Of Engagement Activity | 2019 |
| 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 | Talk at University of Leicester |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | Talk about my research delivered to an audience including PGR and UG students at University of Leicester |
| Year(s) Of Engagement Activity | 2023 |
| 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 |
| Description | You and Your Microbes Outreach Event (part of European Researchers Night and University of Manchester Community Festival) |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Public/other audiences |
| Results and Impact | Organised a exhibition stand at Manchester Museum and the University of Manchester Community Festival where we engaged with participants on (1) antibiotic resistance and (2) the skin microbiome. Participants gave a 'thumbprint' on agar plates, took them home and watched them grow over the course of a week. They tweeted photos of the progress to our social media account. |
| Year(s) Of Engagement Activity | 2019,2022 |
| URL | https://twitter.com/mermanchester |