Improved molecular and computational approaches for the detection of antimicrobial resistance in pathogenic bacteria

Danielle Madden

doi:10.25907/00735

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Improved molecular and computational approaches for the detection of antimicrobial resistance in pathogenic bacteria

Dissertation

Open access

Improved molecular and computational approaches for the detection of antimicrobial resistance in pathogenic bacteria

Danielle Madden

University of the Sunshine Coast, Queensland

Doctor of Philosophy, University of the Sunshine Coast, Queensland

2023

DOI:

https://doi.org/10.25907/00735

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Thesis Open Access

Abstract

Microbiology

Genetics

Public health

antimicrobial resistance

whole-genome sequencing

prediction

ARDaP

diagnostics

real-time PCR

quantitative PCR

gene expression

mutation

antibiotic

multidrug resistance

Antimicrobial resistance (AMR) represents one of the most critical and growing threats to global health. AMR, which is already associated with significant social and economic burden, is predicted to further worsen, causing 10 million deaths annually by 2050 if left unchecked. The scarcity of novel antimicrobial agents, coupled with enduring antibiotic misuse/overuse that has worsened due to the COVID-19 pandemic, has driven the dramatic increase in multidrug-resistant (MDR) bacteria that threatens to push us closer towards a ‘post-antibiotic’ world. To address this existential threat, the overall aim of my thesis was to improve both understanding and diagnosis of AMR infections caused by formidable Gram-negative bacteria. Early and accurate AMR detection is imperative for effective infection control, personalised treatment, and for reducing our heavy reliance on empirical administration of broad-spectrum antibiotics that drive AMR and MDR development. Next-generation sequencing (NGS) has been increasingly implemented as a comprehensive and accurate diagnostic tool in our battle against superbugs. Unfortunately, current bioinformatic software is limited in its ability to detect AMR determinants for many opportunistic bacterial pathogens such as Burkholderia pseudomallei and Pseudomonas aeruginosa. Melioidosis, a rare but often deadly tropical disease caused by B. pseudomallei, requires up to six months of antimicrobial treatment to fully eradicate this pathogen; early identification of AMR development in this pathogen thus represents a clinical imperative to minimise mortality. The ESKAPE pathogen, P. aeruginosa, represents one of the biggest AMR threats globally due to its high intrinsic AMR, MDR potential, highly virulent nature, commonality, and person-to-person transmissibility. Combined, these factors make P. aeruginosa infections exceedingly difficult to treat, resulting in significant morbidity and mortality.

Details

Title: Improved molecular and computational approaches for the detection of antimicrobial resistance in pathogenic bacteria
Authors: Danielle Madden - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Contributors: Erin Price (Supervisor) - University of the Sunshine Coast, Queensland, Centre for Bioinnovation
Derek Sarovich (Supervisor) - University of the Sunshine Coast, Queensland, Centre for Bioinnovation
Awarding institution: University of the Sunshine Coast, Queensland
Degree awarded: Doctor of Philosophy
Publisher: University of the Sunshine Coast, Queensland
DOI: 10.25907/00735
Organisation Unit: University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Centre for Bioinnovation
Language: English
Record Identifier: 99704997902621
Output Type: Dissertation

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