Biochemistry and Pharmacology - Theses
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ItemInvestigating features and interactions of the childhood respiratory microbiome( 2022)The human microbiome is closely linked with the health of an individual and is implicated in numerous complex diseases including diabetes, inflammatory bowel disease, cancer, cystic fibrosis (CF), and asthma. There is growing evidence to suggest the respiratory microbiome influences risk and trajectory of respiratory disease from an early age. Hence, unravelling the biology of the childhood respiratory microbiome is critical to gain a comprehensive understanding of respiratory disease, and requires characterisation of both the aggregate community and individual community members. This thesis strengthens our understanding of the childhood respiratory microbiome through i) investigation of specific community members, Haemophilus influenzae and Haemophilus parainfluenzae, in the context of CF, and ii) exploration of upper respiratory tract (URT) microbiome development during the first year of life with a particular focus on microbe-microbe interactions. While morbidity and mortality of CF principally result from repeated respiratory infections by Pseudomonas aeruginosa, there is emerging evidence that respiratory tract colonisation by Haemophilus species during childhood induces early disease progression. I describe the detection, antimicrobial resistance (AMR), and genome sequencing of H. influenzae and H. parainfluenzae isolated from airway samples of children enrolled in the AREST CF program. This work revealed H. influenzae and H. parainfluenzae carriage rates and strain persistence among participants. Haemophilus isolates were genetically diverse and commonly resistant to antimicrobials with several putative novel resistance determinants identified. Finally, genomic data identified transmission of Haemophilus strains between participants. The association between the respiratory microbiome and respiratory disease has been established in several cohort studies. However, no work has been undertaken to compare preservation of respiratory microbiome dynamics or to reconcile differences between cohort studies. This thesis explores 16S rRNA gene survey data from four longitudinal childhood cohorts, with a focus on microbe-microbe interactions. The URT microbiome composition dynamics during the first year of life are shown to be well preserved across cohorts, and the aggregate data set is leveraged to reveal associations between specific community members and symptoms of acute respiratory illness. A foundation for microbe-microbe interactions during the first year of life is established, which facilitated discovery of two communities that dominate the URT microbiome. For both areas of focus presented in this thesis I additionally developed two novel software tools to support and enhance analysis: hicap, a tool for robust inference of H. influenzae serotype and cap locus structure from WGS data, and FastSpar, a tool for rapid and scalable correlation estimation from compositional data. Collectively, this thesis contributes to our understanding of the childhood URT microbiome in the context of CF and normal development. The results in this thesis provide the first insights into the population dynamics and genomic AMR determinants of H. influenzae and H. parainfluenzae strains in a paediatric CF cohort. The presented findings further recapitulate the most complete overview of URT microbiome development during the first year of life and provide the first foundation for microbe-microbe interaction dynamics. The tools developed and the analyses performed in this thesis provide an important framework for future studies to investigate features and interactions of the respiratory microbiome.
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ItemDetecting horizontal co-transfer of antimicrobial resistance genes in bacteria: a network approach( 2019)Antimicrobials have been widely using as a major resource to treat bacterial infections for almost a century. However, it is not unusual to see antimicrobial resistance emerges in a bacterial species due to natural selection under the usage of antimicrobials. Moreover, numerous studies show that bacteria can accumulate genes encoding resistance to different classes of antimicrobials and share them with other bacteria regardless of ancestry via a biological process called horizontal gene transfer, causing emergence and fast transmission of multidrug resistance. As such, antimicrobial resistance becomes an urgent and global threat to public health, pushing us backwards to the pre-antimicrobial era. In this thesis, I focus on horizontal co-transfer of resistance genes between bacteria of the same species, which is usually caused by co-localisation of resistance genes in mobile genetic elements, also known as physical linkage between these genes. This kind of linkage plays a pivotal role in the evolution of multidrug resistance, because the mobile elements can translocate, recombine and aggregate, rapidly rendering their host bacteria resistant to a wide spectrum of antimicrobials. By far there is nonetheless not an approach identifying horizontally co-transferred genes in a single bacterial species. Yet most authors of literature reported a few co-mobilised resistance genes each time following biological experiments, and some researchers only applied simple association analysis to representative bacterial isolates of distinct species so as to minimise the possibility that a specific combination of genes is inherited from their most-recent common ancestor. In contrast, intra-species association analysis is severely confounded by strong sample relatedness because of bacterial clonal reproduction. This obstacle leaves a gap between the known high frequency of intra-species horizontal gene transfer and our understandings of this process. This thesis presents a scalable computational approach that uses whole-genome sequencing data to identify co-transferred antimicrobial resistance genes in bacteria collected in a few decades from the same species. Moreover, it demonstrates applications of the approach to three clinically important pathogens and reports key players, patterns and dynamics underlying the horizontal co-transfer of resistance genes within each species. In the first chapter, I provide a background to antimicrobial resistance, horizontal gene transfer, whole-genome sequencing and contemporary bioinformatic techniques. I also summarise outcomes of horizontal gene co-transfer for characteristics that we can utilise for inference of physical linkage. Finally, I compare several statistical approaches determining pairwise association between presence-absence status of genes or alleles in bacteria to justify the necessity of controlling for sample relatedness in association analysis. For the second chapter, I derived a methodology inferring co-transferred genes by integrating gene detection, de novo genome assembly, core-genome and phylogenetic analysis, linear mixed models, hypothesis tests for effects of sample relatedness and evaluation of consistency in pairwise physical distances between resistance alleles in bacterial genomes. This methodology is designed to overcome limitations of existing methods summarised in the first chapter. Moreover, I show interpretations of expected outcomes and discuss constraints of this approach. The next three chapters present an implementation of my methodology and its applications on antimicrobial resistance genes in three clinically important species of Enterobacteriaceae. First, I conducted an empirical study following a simulation-and-validation strategy on finished-grade full genomes of six strains of Klebsiella pneumoniae to find out an optimal method that measures the pairwise physical distances between alleles in de novo genome assemblies. I found that for each assembly graph, the most accurate measurements are obtained via setting up constraints for both the number of nodes in the graph and the maximum of distance measurements. Second, I developed GeneMates, a computational and integrative software package that implements my methods proposed in the second chapter for the identification of physically linked resistance alleles or for analysing associations between a large number of resistance alleles when controlling for individual relatedness. In particular, GeneMates leverages network topology to identify potential physical linkage between the alleles. For validation, I applied this tool to whole-genome sequencing data of Escherichia coli and Salmonella Typhimurium, whose acquired resistance genes and relevant mobile genetic elements have been well characterised in publications. In result sections, I illustrate clusters of physically linked resistance alleles and discoveries of their vectors. For the last result chapter, I applied GeneMates to genomes of a large global collection of K.pneumoniae strains, which are adept to uptake DNA from various environments. Furthermore, I compared structure and contents of co-localised allele clusters across time and geography and discovered patterns underlying the evolution of multidrug resistance in this species. To conclude, I have developed and implemented a network approach that performs association tests on presence-absence of resistance alleles in a large collection of bacterial isolates of the same species and infers potential horizontally co-transferred alleles. I have validated this approach using known co-mobilisable resistance genes and the approach showed higher statistical power than existing methods. The GeneMates package will become a powerful tool contributing to routine surveillance of antimicrobial resistance and identifications of known and novel mobile genetic elements. In addition, applications of this package to other kinds of bacterial genes is also feasible and convenient.
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ItemPopulation structure and carriage-infection dynamics of Klebsiella pneumoniae( 2018)Klebsiella pneumoniae is an opportunistic pathogen and global cause of hospital-associated (HA) infections. K. pneumoniae is also part of the healthy human microbiome, providing a potential reservoir for infection. However, the frequency of colonisation and its contribution to infections are not well characterised. A prospective, hospital-wide surveillance of all infections attributed to K. pneumoniae – across the Alfred Health network, Melbourne, Australia – was conducted over a one-year period in 2013. Concurrently, patients in the intensive care unit (Alfred Hospital; AH) and geriatric care wards (Caulfield Hospital; CH) were screened for asymptomatic colonisation. Isolates were characterised using whole genome sequencing and antimicrobial susceptibility profiling. This study aimed to address several objectives: i) To investigate the frequency of colonisation in intensive care unit (ICU) patients and whether colonising strains are a source of infection; ii) To investigate the frequency and source of antimicrobial-resistant colonisation or infection in geriatric patients; iii) To characterise the population structure and genetic diversity of K. pneumoniae causing infections in hospital patients. This study estimated community-associated (CA) asymptomatic gastrointestinal carriage among ICU patients was at 6%, rising significantly to 19% among HA individuals, the latter including all identified multi-drug resistant carriage isolates. Many patients had their own unique colonising and infecting strains, often matching within a patient, though there were instances suggestive of transmission. The combination of genomic and epidemiological data supported five clusters of recent patient-to-patient transmission, frequently involving carriage. Among the CH geriatric patients screened, GI carriage rates were 10.8% and 1.7% of patients had extended-spectrum beta-lactamase (ESBL) producing carriage strains. There were three variably MDR lineages observed among multiple patients, and though there was no direct transmission within the CH, there was evidence supporting transmission at the AH prior to CH admission. The major MDR plasmids were identified; all had regions of clustered AMR genes on transposons and/or integrons. Two of the lineages shared variants of the same plasmid indicating transmission of AMR mobile elements between strains. Across all Klebsiella infections extensive diversity was observed including multiple species, lineages, capsule types, and O-antigen types. Most patients had their own unique carriage and infection strains but there were a small number of transmission chains detected. In most transmission cases, the lineages responsible were ESBL, MDR, or carbapenemase producers. Therefore, patients with antimicrobial-resistant strains pose a greater threat to those around them, as these strains were most strongly associated with transmission. This study showed that the majority of hospital Klebsiella infections arise from the host microbiome, though there is also a smaller burden of infection due to transmission of typically antimicrobial-resistant strains. This has important implications for infection prevention and control: people who are not colonised with Klebsiella have low risk of subsequent infection, except for when infections arise from acquisition of other patients’ antimicrobial-resistant strains. This work revealed that screening for colonisation can elucidate; i) which patients are at risk from infection through self-contamination, and ii) the AMR status of patients’ strains and therefore which individuals pose a risk to other patients through potential transmission.