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Development of an enhanced methodology for large-scale detection and quantification of antimicrobial resistant bacteria in livestock

Lee, Zheng Zhou (2021) Development of an enhanced methodology for large-scale detection and quantification of antimicrobial resistant bacteria in livestock. PhD thesis, Murdoch University.

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Antimicrobial resistance (AMR) is a global health challenge for both humans and animals. A potential source of antimicrobial resistant bacteria is in livestock due to the widespread and unrestrained use of antimicrobials. This is further exacerbated by the presence of bacteria resistant to critically important antimicrobials (CIAs) that are classified as the last-line of treatment of infectious diseases in humans. AMR surveillance in livestock has become a key cornerstone of AMR control strategies by informing the presence and frequency of resistance including CIA-resistant bacteria. Established approach of AMR surveillance in livestock typically have a national-level focus that only acquire a maximum of 300 isolates nationwide for antimicrobial susceptibility testing (AST), with each isolate representing one sample from one farm. While this approach is sufficient for evaluating AMR at national-level, it is inadequate for AMR surveillance at herd-level as one isolate is not sufficient to represent AMR of each farm, leading to errors when implementing antimicrobial stewardship and AMR control measures at the herd-level. This project aimed to address this issue by developing an enhanced AMR surveillance method that combines a multiple samples per herd approach with automated laboratory robotics and selective agars incorporated with antimicrobials to provide accurate large-scale data on the presence, frequency and carriage levels of resistant bacteria within individual farms.

The first step in developing the enhanced method was validating suitable selective agars for enumeration of resistant E. coli colonies. Of the three E. coli selective agars compared, MacConkey agar was found to be consistently inferior in E. coli growth performance than the two modern commercially available E. coli selective agars, Brilliance™ E. coli and CHROMagar™ ECC. This inferiority in E. coli growth performance was consistently seen regardless of whether pure cultures or homogenised faecal samples were used for inoculation onto E. coli selective agar with or without incorporation of antimicrobials. Brilliance™ ESBL and CHROMagar™ ESBL which are two modern commercially available selective agar targeting extended-spectrum cephalosporin (ESC)-resistant E. coli were also compared to determine which is better suited for quantifying ESC-resistant E. coli. The latter was found to be more suitable compared to the former due to being able to support a wider diversity of ESC-resistant E. coli strains.

The chosen selective agars were subsequently applied to the enhanced method to describe the CIA-resistance scenario of Australian pigs in order demonstrate its capability to provide a more accurate and detailed AMR data at the herd, state and national-level. A major finding was the detection of CIA-resistant E. coli in Australian pigs. Fluoroquinolone (FQ)-resistant E. coli was present among majority of Australian pig farms nationwide, while the presence of ESC-resistant E. coli was detected among eight Australian pig farms nationwide, with the former having a higher frequency compared to the latter. However, compared to the commensal E. coli population, carriage levels of both resistant E. coli were lower, indicating that CIA-resistant E. coli has not yet spread throughout the commensal E. coli population. When subjected to AST, CIA-resistant E. coli harbouring phenotypic resistance towards FQ and ESC was detected but due to the nature of FQ-resistance mechanisms, it has limited clinical relevance. Whole genome sequencing (WGS) was also performed on CIA-resistant E. coli which revealed that ST744 and ST4981 are the current dominant FQ-resistant E. coli and ESC-resistant E. coli sequence types (STs) respectively present among Australian pigs nationwide. Further analysis suggests that both STs were likely introduced into Australian pigs via external sources. Nonetheless, the multiple samples per herd approach and quantitative focus of the enhanced method demonstrated that it is capable of delivering a more accurate and detailed AMR data at the herd-level compared to established AMR surveillance systems.

The adaptability of the enhanced method towards a different livestock species was demonstrated through the performance of AMR surveillance on ten Australian meat chicken farms. While ESC-resistant E. coli was not detected, ciprofloxacin-resistant E. coli was detected on all farms, with carriage levels that were lower than commensal E. coli. This indicates that FQ-resistant E. coli is present among all ten farms but has not yet spread throughout its commensal E. coli population. When subjected to AST, only 57.1% of FQ-resistant E. coli isolates were multi-class resistant, and that the most common phenotypic resistance profile was one with resistance towards two antimicrobial classes. Though WGS will be conducted to ascertain the genomic characteristics of FQ-resistant E. coli isolates in these ten farms, the findings demonstrated that the enhanced method is also capable of delivering the same accurate and detailed AMR data at the flock-level for meat chickens.

In conclusion, the findings demonstrated that the enhanced method is capable of delivering a more accurate and detailed AMR data than established AMR surveillance systems for livestock at all levels of governance, and with different livestock species. This ultimately leads to improved judgements when implementing AMR control strategies as part of biosecurity protocols to prevent further emergence and spread of CIA-resistant E. coli. Additionally, it provides further prospects for expanding the application of the enhanced method within the food and public health sectors, with further opportunities for enhancement via the inclusion of data pertaining to antimicrobial use and resistance transmission pathways.

Item Type: Thesis (PhD)
Murdoch Affiliation(s): Veterinary Medicine
Supervisor(s): Abraham, Sam, Abraham, Rebecca and Sahibzada, Shafi
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