Computational profiling of antimicrobial resistance genes and mobile genetic elements in the human microbiome

Student thesis: Doctoral ThesisDoctor of Philosophy


Antimicrobial resistance is one of the greatest global health threats of this generation. Antimicrobial resistance in pathogens is leading to infections becoming untreatable with antimicrobial chemotherapy treatments. The number of different types of antimicrobial drugs are limited, meaning pathogens are rapidly developing resistance to commonly used antimicrobial drugs. This has been driven by the overuse of antimicrobial drugs in health care and agriculture, leading pathogens to evolve biological mechanisms to adapt to anthropogenic levels of antimicrobials. Microorganisms, including antimicrobial resistant pathogens, spread and colonise between animals, humans and the environment. Microorganisms have another insidious mechanism of spreading antimicrobial resistance, which is by transferring their genetic resistance determinants between their genomes. This process, known as horizontal gene transfer, has enabled pathogens to acquire antimicrobial resistance genes from other non-pathogenic and pathogenic microbes in close proximity within microbial communities. These antimicrobial resistance genes are usually carried by mobile genetic elements that can integrate into the genome of these pathogens.
Global surveillance using whole genome sequencing and molecular techniques have been adopted to monitor the spread, genetic evolution and resistance severity of antimicrobial resistant pathogens in human and animal populations. Whole genome sequencing has allowed scientists to determine antimicrobial resistance genes in microbial genomes that cause antimicrobial resistance, and in some cases, how these may have been acquired, e.g. carried by mobile genetic elements. Classical surveillance techniques rely on sequencing a single genome from an isolated, cultured strain. However, this cannot be achieved for microbes that are unculturable. Further, it is incredibly labour-intensive to characterise genomes from all possible strains across microbial communities. Metagenomic sequencing is a more rapid approach that sequences as many genomes from a microbial community as possible, without relying on culturing. Metagenomics has revolutionised the ability to characterise genomes from a variety of species, including profiling antimicrobial resistance genes and mobile genetic elements. A caveat with metagenomics is that it is unable to directly show whether microbes in the community produce antimicrobial resistance traits, which can be achieved with culture-based techniques. However, advances in sequencing technologies and computational methods to interpret metagenomic data may help predict how antimicrobial resistance genes and mobile genetic elements lead to antimicrobial resistance in clinical settings.
In this study, I developed computational tools to profile antimicrobial resistance genes and three types of mobile genetic elements: bacteriophages, plasmids and insertion sequences/unit transposons, from whole, short-read metagenomic data. These tools were applied to publicly available metagenomic sequences of microbial communities in the human gastrointestinal tract across different countries worldwide. This study presents the first attempt at comparing the antimicrobial resistance gene profiles and their associations with mobile genetic elements from metagenomes between sites in the oral cavity and the gut with computational methods. Differences between these profiles are found particularly between gut and oral sites. The gut, surface of the tongue and dental plaque host the greatest diversity of antimicrobial resistance genes and mobile genetic elements. Antimicrobial resistance genes are rarely found on bacteriophages, but are commonly associated with plasmids and insertion sequences. Insertion sequences are found to be associated with a greater diversity of antimicrobial resistance genes than plasmids, but plasmids encoding antimicrobial resistance genes are highly prevalent. These methodologies and results provide a framework for future development in surveillance and clinical predictions of antimicrobial resistance using metagenomic sequencing technologies.
Date of Award1 Feb 2021
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorDave Moyes (Supervisor) & David Gomez Cabrero Lopez (Supervisor)

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