Microbiology & Immunology - Research Publications
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ItemNo Preview AvailableBioinformatic investigation of discordant sequence data for SARS- CoV-2: insights for robust genomic analysis during pandemic surveillance(MICROBIOLOGY SOC, 2023-11)The COVID-19 pandemic has necessitated the rapid development and implementation of whole-genome sequencing (WGS) and bioinformatic methods for managing the pandemic. However, variability in methods and capabilities between laboratories has posed challenges in ensuring data accuracy. A national working group comprising 18 laboratory scientists and bioinformaticians from Australia and New Zealand was formed to improve data concordance across public health laboratories (PHLs). One effort, presented in this study, sought to understand the impact of the methodology on consensus genome concordance and interpretation. SARS-CoV-2 WGS proficiency testing programme (PTP) data were retrospectively obtained from the 2021 Royal College of Pathologists of Australasia Quality Assurance Programmes (RCPAQAP), which included 11 participating Australian laboratories. The submitted consensus genomes and reads from eight contrived specimens were investigated, focusing on discordant sequence data and findings were presented to the working group to inform best practices. Despite using a variety of laboratory and bioinformatic methods for SARS-CoV-2 WGS, participants largely produced concordant genomes. Two participants returned five discordant sites in a high-Cτ replicate, which could be resolved with reasonable bioinformatic quality thresholds. We noted ten discrepancies in genome assessment that arose from nucleotide heterogeneity at three different sites in three cell-culture-derived control specimens. While these sites were ultimately accurate after considering the participants' bioinformatic parameters, it presented an interesting challenge for developing standards to account for intrahost single nucleotide variation (iSNV). Observed differences had little to no impact on key surveillance metrics, lineage assignment and phylogenetic clustering, while genome coverage <90 % affected both. We recommend PHLs bioinformatically generate two consensus genomes with and without ambiguity thresholds for quality control and downstream analysis, respectively, and adhere to a minimum 90 % genome coverage threshold for inclusion in surveillance interpretations. We also suggest additional PTP assessment criteria, including primer efficiency, detection of iSNVs and minimum genome coverage of 90 %. This study underscores the importance of multidisciplinary national working groups in informing guidelines in real time for bioinformatic quality acceptance criteria. It demonstrates the potential for enhancing public health responses through improved data concordance and quality control in SARS-CoV-2 genomic analysis during pandemic surveillance.
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ItemNo Preview AvailableHigh-performance enrichment-based genome sequencing to support the investigation of hepatitis A virus outbreaks(AMER SOC MICROBIOLOGY, 2024-01-11)This proof-of-concept study introduces a hybrid capture oligo panel for whole-genome sequencing of all six human pathogenic hepatitis A virus (HAV) subgenotypes, exhibiting a higher sensitivity than some conventional genotyping assays. The ability of hybrid capture to enrich multiple targets allows for a single, streamlined workflow, thus facilitating the potential harmonization of molecular surveillance of HAV with other enteric viruses. Even challenging sample matrices can be accommodated, making them suitable for broad implementation in clinical and public health laboratories. This innovative approach has significant implications for enhancing multijurisdictional outbreak investigations as well as our understanding of the global diversity and transmission dynamics of HAV.
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ItemNo Preview AvailableHigh performance Legionella pneumophila source attribution using genomics-based machine learning classification(AMER SOC MICROBIOLOGY, 2024-02-21)Fundamental to effective Legionnaires' disease outbreak control is the ability to rapidly identify the environmental source(s) of the causative agent, Legionella pneumophila. Genomics has revolutionized pathogen surveillance, but L. pneumophila has a complex ecology and population structure that can limit source inference based on standard core genome phylogenetics. Here, we present a powerful machine learning approach that assigns the geographical source of Legionnaires' disease outbreaks more accurately than current core genome comparisons. Models were developed upon 534 L. pneumophila genome sequences, including 149 genomes linked to 20 previously reported Legionnaires' disease outbreaks through detailed case investigations. Our classification models were developed in a cross-validation framework using only environmental L. pneumophila genomes. Assignments of clinical isolate geographic origins demonstrated high predictive sensitivity and specificity of the models, with no false positives or false negatives for 13 out of 20 outbreak groups, despite the presence of within-outbreak polyclonal population structure. Analysis of the same 534-genome panel with a conventional phylogenomic tree and a core genome multi-locus sequence type allelic distance-based classification approach revealed that our machine learning method had the highest overall classification performance-agreement with epidemiological information. Our multivariate statistical learning approach maximizes the use of genomic variation data and is thus well-suited for supporting Legionnaires' disease outbreak investigations.IMPORTANCEIdentifying the sources of Legionnaires' disease outbreaks is crucial for effective control. Current genomic methods, while useful, often fall short due to the complex ecology and population structure of Legionella pneumophila, the causative agent. Our study introduces a high-performing machine learning approach for more accurate geographical source attribution of Legionnaires' disease outbreaks. Developed using cross-validation on environmental L. pneumophila genomes, our models demonstrate excellent predictive sensitivity and specificity. Importantly, this new approach outperforms traditional methods like phylogenomic trees and core genome multi-locus sequence typing, proving more efficient at leveraging genomic variation data to infer outbreak sources. Our machine learning algorithms, harnessing both core and accessory genomic variation, offer significant promise in public health settings. By enabling rapid and precise source identification in Legionnaires' disease outbreaks, such approaches have the potential to expedite intervention efforts and curtail disease transmission.
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ItemNo Preview AvailableThe two-component system WalKR provides an essential link between cell wall homeostasis and DNA replication in Staphylococcus aureus(AMER SOC MICROBIOLOGY, 2023-12-19)Among the 16 two-component systems in the opportunistic human pathogen Staphylococcus aureus, only WalKR is essential. Like the orthologous systems in other Bacillota, S. aureus WalKR controls autolysins involved in peptidoglycan remodeling and is therefore intimately involved in cell division. However, despite the importance of WalKR in S. aureus, the basis for its essentiality is not understood and the regulon is poorly defined. Here, we defined a consensus WalR DNA-binding motif and the direct WalKR regulon by using functional genomics, including chromatin immunoprecipitation sequencing, with a panel of isogenic walKR mutants that had a spectrum of altered activities. Consistent with prior findings, the direct regulon includes multiple autolysin genes. However, this work also revealed that WalR directly regulates at least five essential genes involved in lipoteichoic acid synthesis (ltaS): translation (rplK), DNA compaction (hup), initiation of DNA replication (dnaA, hup) and purine nucleotide metabolism (prs). Thus, WalKR in S. aureus serves as a polyfunctional regulator that contributes to fundamental control over critical cell processes by coordinately linking cell wall homeostasis with purine biosynthesis, protein biosynthesis, and DNA replication. Our findings further address the essentiality of this locus and highlight the importance of WalKR as a bona fide target for novel anti-staphylococcal therapeutics. IMPORTANCE The opportunistic human pathogen Staphylococcus aureus uses an array of protein sensing systems called two-component systems (TCS) to sense environmental signals and adapt its physiology in response by regulating different genes. This sensory network is key to S. aureus versatility and success as a pathogen. Here, we reveal for the first time the full extent of the regulatory network of WalKR, the only staphylococcal TCS that is indispensable for survival under laboratory conditions. We found that WalKR is a master regulator of cell growth, coordinating the expression of genes from multiple, fundamental S. aureus cellular processes, including those involved in maintaining cell wall metabolism, protein biosynthesis, nucleotide metabolism, and the initiation of DNA replication.
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ItemNo Preview AvailableGenomic surveillance for antimicrobial resistance - a One Health perspective(NATURE PORTFOLIO, 2024-02)Antimicrobial resistance (AMR) - the ability of microorganisms to adapt and survive under diverse chemical selection pressures - is influenced by complex interactions between humans, companion and food-producing animals, wildlife, insects and the environment. To understand and manage the threat posed to health (human, animal, plant and environmental) and security (food and water security and biosecurity), a multifaceted 'One Health' approach to AMR surveillance is required. Genomic technologies have enabled monitoring of the mobilization, persistence and abundance of AMR genes and mutations within and between microbial populations. Their adoption has also allowed source-tracing of AMR pathogens and modelling of AMR evolution and transmission. Here, we highlight recent advances in genomic AMR surveillance and the relative strengths of different technologies for AMR surveillance and research. We showcase recent insights derived from One Health genomic surveillance and consider the challenges to broader adoption both in developed and in lower- and middle-income countries.
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ItemNo Preview AvailableA statistical genomics framework to trace bacterial genomic predictors of clinical outcomes in Staphylococcus aureus bacteremia(Elsevier, 2023-09-26)Outcomes of severe bacterial infections are determined by the interplay between host, pathogen, and treatments. While human genomics has provided insights into host factors impacting Staphylococcus aureus infections, comparatively little is known about S. aureus genotypes and disease severity. Building on the hypothesis that bacterial pathoadaptation is a key outcome driver, we developed a genome-wide association study (GWAS) framework to identify adaptive mutations associated with treatment failure and mortality in S. aureus bacteremia (1,358 episodes). Our research highlights the potential of vancomycin-selected mutations and vancomycin minimum inhibitory concentration (MIC) as key explanatory variables to predict infection severity. The contribution of bacterial variation was much lower for clinical outcomes (heritability <5%); however, GWASs allowed us to identify additional, MIC-independent candidate pathogenesis loci. Using supervised machine learning, we were able to quantify the predictive potential of these adaptive signatures. Our statistical genomics framework provides a powerful means to capture adaptive mutations impacting severe bacterial infections.
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ItemNo Preview AvailableThe importance of utilizing travel history metadata for informative phylogeographical inferences: a case study of early SARS-CoV-2 introductions into Australia(MICROBIOLOGY SOC, 2023-08)Inferring the spatiotemporal spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via Bayesian phylogeography has been complicated by the overwhelming sampling bias present in the global genomic dataset. Previous work has demonstrated the utility of metadata in addressing this bias. Specifically, the inclusion of recent travel history of SARS-CoV-2-positive individuals into extended phylogeographical models has demonstrated increased accuracy of estimates, along with proposing alternative hypotheses that were not apparent using only genomic and geographical data. However, as the availability of comprehensive epidemiological metadata is limited, many of the current estimates rely on sequence data and basic metadata (i.e. sample date and location). As the bias within the SARS-CoV-2 sequence dataset is extensive, the degree to which we can rely on results drawn from standard phylogeographical models (i.e. discrete trait analysis) that lack integrated metadata is of great concern. This is particularly important when estimates influence and inform public health policy. We compared results generated from the same dataset, using two discrete phylogeographical models: one including travel history metadata and one without. We utilized sequences from Victoria, Australia, in this case study for two unique properties. Firstly, the high proportion of cases sequenced throughout 2020 within Victoria and the rest of Australia. Secondly, individual travel history was collected from returning travellers in Victoria during the first wave (January to May) of the coronavirus disease 2019 (COVID-19) pandemic. We found that the implementation of individual travel history was essential for the estimation of SARS-CoV-2 movement via discrete phylogeography models. Without the additional information provided by the travel history metadata, the discrete trait analysis could not be fit to the data due to numerical instability. We also suggest that during the first wave of the COVID-19 pandemic in Australia, the primary driving force behind the spread of SARS-CoV-2 was viral importation from international locations. This case study demonstrates the necessity of robust genomic datasets supplemented with epidemiological metadata for generating accurate estimates from phylogeographical models in datasets that have significant sampling bias. For future work, we recommend the collection of metadata in conjunction with genomic data. Furthermore, we highlight the risk of applying phylogeographical models to biased datasets without incorporating appropriate metadata, especially when estimates influence public health policy decision making.
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ItemImproved Genome Sequence of Australian Methicillin-Resistant Staphylococcus aureus Strain JKD6159(AMER SOC MICROBIOLOGY, 2023-02-16)Staphylococcus aureus strain JKD6159 represents a prominent community-acquired methicillin-resistant S. aureus (MRSA) clone in Australia. Here, we report an improved assembly of the original S. aureus JKD6159 genome sequence. By using deep sequencing with multiple technologies combined with carefully curated assembly and polishing, we believe the assembly to contain zero errors.
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ItemAn ISO-certified genomics workflow for identification and surveillance of antimicrobial resistance(NATURE PORTFOLIO, 2023-01-04)Realising the promise of genomics to revolutionise identification and surveillance of antimicrobial resistance (AMR) has been a long-standing challenge in clinical and public health microbiology. Here, we report the creation and validation of abritAMR, an ISO-certified bioinformatics platform for genomics-based bacterial AMR gene detection. The abritAMR platform utilises NCBI's AMRFinderPlus, as well as additional features that classify AMR determinants into antibiotic classes and provide customised reports. We validate abritAMR by comparing with PCR or reference genomes, representing 1500 different bacteria and 415 resistance alleles. In these analyses, abritAMR displays 99.9% accuracy, 97.9% sensitivity and 100% specificity. We also compared genomic predictions of phenotype for 864 Salmonella spp. against agar dilution results, showing 98.9% accuracy. The implementation of abritAMR in our institution has resulted in streamlined bioinformatics and reporting pathways, and has been readily updated and re-verified. The abritAMR tool and validation datasets are publicly available to assist laboratories everywhere harness the power of AMR genomics in professional practice.
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ItemDrug resistance and genetic profile of bacterial species associated with Buruli ulcer wound infections in two districts of Ghana(BMJ, 2017-02)Background: We identified secondary infection of Buruli ulcer (BU) wounds as a cause of healing delay. In order to contribute to the improvement of wound management and reduction of healing delay, we initiated a study to gain understanding of the possible routes of infection and also characterised the resistant profiles of Gram negative bacteria isolated from the wounds of patients attending two health facilities in Ghana. Methods: Staphylococcus aureus isolates were characterised by the spa gene, mecA and the Pantone Valentine Leukocidin (PVL) toxin followed by spa sequencing and whole genome sequencing of a subset of isolates. Phenotypic antibiotic susceptibility testing of Gram negative clinical isolates was performed and multidrug-resistant Pseudomonas aeruginosa identified. The Enterobacteriaceae were further investigated for ESBL and carbapenem production, and some resistance conferring genes were analysed by PCR. Results: Twenty-four isolates were identified as methicillin-resistant S. aureus (MRSA), and lukFS genes encoding PVL were identified in 67 isolates. Typing and sequencing of the spa gene from 91 isolates identified 29 different spa types with t355 (ST152), t186 (ST88), and t346 dominating. While many distinct strains were isolated from both health centres, genotype clustering was identified within centres pointing to possible health care-associated transmission. Phylogenomic analysis confirmed these clusters. Among the GNB, phenotype screening showed widespread resistance to ampicillin, chloramphenicol, ticarcillin-clavulanic acid, cefuroxime and sulphamethoxazole-trimethoprim. ESBL production was confirmed in 15 isolates phenotypically while 61.5% of screen-positive isolates harboured at least one ESBL-conferring gene. Carbapenem encoding genes were detected in 41% of the isolates. Conclusions: Our findings indicate that the health-care environment likely contributes to superinfection of BU wounds and calls for training in wound management and infection control techniques. The observed frequency of ESBL and carbapenem resistance indicates the need to set up surveillance networks and strictly enforce policies which guide the rational use of antibiotics.