Genome scale metabolic modelling to identify metabolic responses of resistant bacteria to antibiotic challenges
type: CreativeWork
description: Antimicrobial Resistance (AMR) is one of the most alarming public health threats being faced by humanity in present times, in every aspect from economic losses to social instability. The pace of antimicrobial target research is being overtaken by the pace of resistance developing among disease causing pathogens. In order to explore solutions for AMR, INNOTARGETS , a consortium of academic and industrial partners has been established, with the objective of applying innovative approaches for identifying metabolic drug targets in resistant pathogenic bacteria. In this thesis, genome-scale metabolic modelling (GSMM) was applied to identify antimicrobial drug targets. Experimental data from transposon mutant libraries grown in presence of tilmicosin, a macrolide antibiotic was integrated into GSM analyses. The GSMs of two strains of E. coli, including a novel clinical strain were constructed and analysed. A potential pathway involved in lipopolysaccharide and cell wall synthesis was identified as a response to tilmicosin treatment as a resistance mechanism. The enzymes involved in this pathway could be potential helper drug targets along with macrolide treatment. In order to understand the metabolic behaviour of E. coli in control condition in a defined media, external metabolomics measurements were taken and incorporated into model analysis. The preference for producing glycine as a by-product, instead of consuming it from media was an interesting observation. During anaerobic growth conditions, utilisation of L-aspartate as a substrate and fumarate as a terminal electron acceptor was also identified. A novel method comparing in silico knockouts in models and essential genes determined by transposon mutagenesis was developed as a part of this study. Contingency table analysis showed a high degree of agreement between model and experimental knockouts. It was inferred that reaction knockouts in GSM were in concordance with experimental gene knockouts.
abstract: Antimicrobial Resistance (AMR) is one of the most alarming public health threats being faced by humanity in present times, in every aspect from economic losses to social instability. The pace of antimicrobial target research is being overtaken by the pace of resistance developing among disease causing pathogens. In order to explore solutions for AMR, INNOTARGETS , a consortium of academic and industrial partners has been established, with the objective of applying innovative approaches for identifying metabolic drug targets in resistant pathogenic bacteria. In this thesis, genome-scale metabolic modelling (GSMM) was applied to identify antimicrobial drug targets. Experimental data from transposon mutant libraries grown in presence of tilmicosin, a macrolide antibiotic was integrated into GSM analyses. The GSMs of two strains of E. coli, including a novel clinical strain were constructed and analysed. A potential pathway involved in lipopolysaccharide and cell wall synthesis was identified as a response to tilmicosin treatment as a resistance mechanism. The enzymes involved in this pathway could be potential helper drug targets along with macrolide treatment. In order to understand the metabolic behaviour of E. coli in control condition in a defined media, external metabolomics measurements were taken and incorporated into model analysis. The preference for producing glycine as a by-product, instead of consuming it from media was an interesting observation. During anaerobic growth conditions, utilisation of L-aspartate as a substrate and fumarate as a terminal electron acceptor was also identified. A novel method comparing in silico knockouts in models and essential genes determined by transposon mutagenesis was developed as a part of this study. Contingency table analysis showed a high degree of agreement between model and experimental knockouts. It was inferred that reaction knockouts in GSM were in concordance with experimental gene knockouts.
author: Verma, Pareena
language: en
date modified: 2026-07-24T03:42:04Z
provider: Oxford Brookes University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Oxford Brookes University

PropertyValue
property ID: DOI
value: 10.24384/vfad-pt17