Mathematical approaches to investigate energy dependence in genetic pathways involved with antimicrobial resistance

Kerr, Ryan David Thomas ORCID: 0000-0002-3454-7640 (2022). Mathematical approaches to investigate energy dependence in genetic pathways involved with antimicrobial resistance. University of Birmingham. Ph.D.

Preview

Kerr2022PhD.pdf
Text - Accepted Version
Available under License All rights reserved.
Download (11MB) | Preview

Abstract

Antimicrobial resistance is becoming increasingly common and is causing a global health crisis. Microorganisms have developed numerous sophisticated resistance mechanisms contributing to the reduced efficacy of antimicrobials. Combined with slow development of new antimicrobial drugs (including antibiotics) the possibility of a post-antimicrobial era is growing. To combat this global issue, we need to develop new treatments for microbial infections, and engineering novel targets could be a solution.

Genetically identical cells can display significantly different phenotypes in the same environment. Developing our understanding of how bacterial cells support and generate different phenotypes without changes to their DNA is critical in combatting antimicrobial resistance, as some cell fate decisions (such as generating persister cells) enable cells to tolerate high concentrations of antibiotics and outlast antibiotic treatment. Consequently, persister cells, and other antimicrobial resistance phenotypes, reduce the efficacy of antibiotics, and increase the difficulty of eradicating infections.

To do this, we first need to better understand how cells in an isogenic population generate different phenotypes. Functionally vital cell fate decisions from a range of phenotypic choices are made by regulatory networks, the dynamics of which rely on gene expression and hence depend on cellular energy availability, particularly ATP levels. However, despite pronounced cell-to-cell ATP differences observed across biological systems, the influence of energy availability on regulatory network dynamics is often overlooked as modulating cellular decision-making and phenotypic variation, limiting our knowledge of how energy budgets affect cell behaviour.

This study will introduce and analyse the behaviour of gene regulatory networks regulating cellular decisions when cell-to-cell ATP variability is considered. Results for a simple generalisable network will be presented, and then the model will be extended to develop a more scalable and general modelling framework for analysing the effects of energy availability within a clonal population on the dynamics of regulatory networks. This framework is then applied to a gene regulatory network underlying a clinically-important antimicrobial resistance mechanism.

An alternate mathematical modelling investigation into antimicrobial resistance will then be introduced. In this approach, a framework called HyperTraPS is applied to resistance profiles of Pseudomonas aeruginosa samples extracted from patients being treated in hospitals located in Mexico. Results from this framework allow the inference of possible pathways leading to the acquisition of resistance to a set of antibiotics commonly used in the treatment of this clinically-relevant bacterial species.

Throughout this study we will demonstrate that energetic differences between cells may be an important consideration to help explain observed variability in cellular decision-making across biological systems, including those decisions promoting resistance, and provide a novel target for future therapeutics.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):