Microbiology & Immunology - Research Publications

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    Mosquitoes provide a transmission route between possums and humans for Buruli ulcer in southeastern Australia
    Mee, PT ; Buultjens, AH ; Oliver, J ; Brown, K ; Crowder, JC ; Porter, JL ; Hobbs, EC ; Judd, LM ; Taiaroa, G ; Puttharak, N ; Williamson, DA ; Blasdell, KR ; Tay, EL ; Feldman, R ; Muzari, MO ; Sanders, C ; Larsen, S ; Crouch, SR ; Johnson, PDR ; Wallace, JR ; Price, DJ ; Hoffmann, AA ; Gibney, KB ; Stinear, TP ; Lynch, SE (NATURE PORTFOLIO, 2024-02)
    Buruli ulcer, a chronic subcutaneous infection caused by Mycobacterium ulcerans, is increasing in prevalence in southeastern Australia. Possums are a local wildlife reservoir for M. ulcerans and, although mosquitoes have been implicated in transmission, it remains unclear how humans acquire infection. We conducted extensive field survey analyses of M. ulcerans prevalence among mosquitoes in the Mornington Peninsula region of southeastern Australia. PCR screening of trapped mosquitoes revealed a significant association between M. ulcerans and Aedes notoscriptus. Spatial scanning statistics revealed overlap between clusters of M. ulcerans-positive Ae. notoscriptus, M. ulcerans-positive possum excreta and Buruli ulcer cases, and metabarcoding analyses showed individual mosquitoes had fed on humans and possums. Bacterial genomic analysis confirmed shared single-nucleotide-polymorphism profiles for M. ulcerans detected in mosquitoes, possum excreta and humans. These findings indicate Ae. notoscriptus probably transmit M. ulcerans in southeastern Australia and highlight mosquito control as a Buruli ulcer prevention measure.
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    Bioinformatic investigation of discordant sequence data for SARS- CoV-2: insights for robust genomic analysis during pandemic surveillance
    Zufan, SE ; Lau, KA ; Donald, A ; Hoang, T ; Foster, CSP ; Sikazwe, C ; Theis, T ; Rawlinson, WD ; Ballard, SA ; Stinear, TP ; Howden, BP ; Jennison, AV ; Seemann, T (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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    High-performance enrichment-based genome sequencing to support the investigation of hepatitis A virus outbreaks
    Zufan, SE ; Mercoulia, K ; Kwong, JC ; Judd, LM ; Howden, BP ; Seemann, T ; Stinear, TP ; Rantsiou, K (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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    High performance Legionella pneumophila source attribution using genomics-based machine learning classification
    Buultjens, AH ; Vandelannoote, K ; Mercoulia, K ; Ballard, S ; Sloggett, C ; Howden, BP ; Seemann, T ; Stinear, TP ; Vives, M ; Vives, M (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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    A human model of Buruli ulcer: The case for controlled human infection and considerations for selecting a Mycobacterium ulcerans challenge strain
    Muhi, S ; Osowicki, J ; O'Brien, D ; Johnson, PDR ; Pidot, S ; Doerflinger, M ; Marshall, JLL ; Pellegrini, M ; McCarthy, J ; Stinear, TPP ; Converse, PJ (PUBLIC LIBRARY SCIENCE, 2023-06)
    Critical knowledge gaps regarding infection with Mycobacterium ulcerans, the cause of Buruli ulcer (BU), have impeded development of new therapeutic approaches and vaccines for prevention of this neglected tropical disease. Here, we review the current understanding of host-pathogen interactions and correlates of immune protection to explore the case for establishing a controlled human infection model of M. ulcerans infection. We also summarise the overarching safety considerations and present a rationale for selecting a suitable challenge strain.
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    The two-component system WalKR provides an essential link between cell wall homeostasis and DNA replication in Staphylococcus aureus
    Sharkey, LKR ; Guerillot, R ; Walsh, CJ ; Turner, AM ; Lee, JYH ; Neville, SL ; Klatt, S ; Baines, SL ; Pidot, SJ ; Rossello, FJ ; Seemann, T ; McWilliam, HEG ; Cho, E ; Carter, GP ; Howden, BP ; McDevitt, CA ; Hachani, A ; Stinear, TP ; Monk, IR ; Torres, VJ (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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    Comparison of contemporary invasive and non-invasive Streptococcus pneumoniae isolates reveals new insights into circulating anti-microbial resistance determinants
    Higgs, C ; Kumar, LS ; Stevens, K ; Strachan, J ; Korman, T ; Horan, K ; Daniel, D ; Russell, M ; Mcdevitt, CA ; Sherry, NL ; Stinear, TP ; Howden, BP ; Gorrie, CL ; Tamma, PD (AMER SOC MICROBIOLOGY, 2023-10-12)
    Streptococcus pneumoniae is a major human pathogen with a high burden of disease. Non-invasive isolates (those found in non-sterile sites) are thought to be a key source of invasive isolates (those found in sterile sites) and a reservoir of anti-microbial resistance (AMR) determinants. Despite this, pneumococcal surveillance has almost exclusively focused on invasive isolates. We aimed to compare contemporaneous invasive and non-invasive isolate populations to understand how they interact and identify differences in AMR gene distribution. We used a combination of whole-genome sequencing and phenotypic anti-microbial susceptibility testing and a data set of invasive (n = 1,288) and non-invasive (n = 186) pneumococcal isolates, collected in Victoria, Australia, between 2018 and 2022. The non-invasive population had increased levels of antibiotic resistance to multiple classes of antibiotics including beta-lactam antibiotics penicillin and ceftriaxone. We identified genomic intersections between the invasive and non-invasive populations and no distinct phylogenetic clustering of the two populations. However, this analysis revealed sub-populations overrepresented in each population. The sub-populations that had high levels of AMR were overrepresented in the non-invasive population. We determined that WamR-Pneumo was the most accurate in silico tool for predicting resistance to the antibiotics tested. This tool was then used to assess the allelic diversity of the penicillin-binding protein genes, which acquire mutations leading to beta-lactam antibiotic resistance, and found that they were highly conserved (≥80% shared) between the two populations. These findings show the potential of non-invasive isolates to serve as reservoirs of AMR determinants.
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    Clinical Staphylococcus aureus inhibits human T-cell activity through interaction with the PD-1 receptor
    Mellergaard, M ; Skovbakke, SL ; Jepsen, SD ; Panagiotopoulou, N ; Hansen, ABR ; Tian, W ; Lund, A ; Hogh, RI ; Moller, SH ; Guerillot, R ; Hayes, AS ; Erikstrup, LT ; Andresen, L ; Peleg, AY ; Larsen, AR ; Stinear, TP ; Handberg, A ; Erikstrup, C ; Howden, BP ; Goletz, S ; Frees, D ; Skov, S ; Pier, GB ; Fey, PD (AMER SOC MICROBIOLOGY, 2023-10-06)
    Therapies that target and aid the host immune defense to repel cancer cells or invading pathogens are rapidly emerging. Antibiotic resistance is among the largest threats to human health globally. Staphylococcus aureus (S. aureus) is the most common bacterial infection, and it poses a challenge to the healthcare system due to its significant ability to develop resistance toward current available therapies. In long-term infections, S. aureus further adapt to avoid clearance by the host immune defense. In this study, we discover a new interaction that allows S. aureus to avoid elimination by the immune system, which likely supports its persistence in the host. Moreover, we find that blocking the specific receptor (PD-1) using antibodies significantly relieves the S. aureus-imposed inhibition. Our findings suggest that therapeutically targeting PD-1 is a possible future strategy for treating certain antibiotic-resistant staphylococcal infections.
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    Population structure, serotype distribution and antibiotic resistance of Streptococcus pneumoniae causing invasive disease in Victoria, Australia
    Higgs, C ; Kumar, LS ; Stevens, K ; Strachan, J ; Sherry, NL ; Horan, K ; Zhang, J ; Stinear, TP ; Howden, BP ; Gorrie, CL (MICROBIOLOGY SOC, 2023-07)
    Streptococcus pneumoniae is a major human pathogen and can cause a range of conditions from asymptomatic colonization to invasive pneumococcal disease (IPD). The epidemiology and distribution of IPD-causing serotypes in Australia has undergone large changes following the introduction of the 7-valent pneumococcal conjugate vaccine (PCV) in 2005 and the 13-valent PCV in 2011. In this study, to provide a contemporary understanding of the IPD causing population in Victoria, Australia, we aimed to examine the population structure and prevalence of antimicrobial resistance using whole-genome sequencing and comprehensive antimicrobial susceptibility data of 1288 isolates collected between 2018 and 2022. We observed high diversity among the isolates with 52 serotypes, 203 sequence types (STs) and 70 Global Pneumococcal Sequencing Project Clusters (GPSCs) identified. Serotypes contained in the 13v-PCV represented 35.3 % (n=405) of isolates. Antimicrobial resistance (AMR) to at least one antibiotic was identified in 23.8 % (n=358) of isolates with penicillin resistance the most prevalent (20.3 %, n=261 using meningitis breakpoints and 5.1 % n=65 using oral breakpoints). Of the AMR isolates, 28 % (n=101) were multidrug resistant (MDR) (resistant to three or more drug classes). Vaccination status of cases was determined for a subset of isolates with 34 cases classified as vaccine failure events (fully vaccinated IPD cases of vaccine serotype). However, no phylogenetic association with failure events was observed. Within the highly diverse IPD population, we identified six high-risk sub-populations of public health concern characterized by high prevalence, high rates of AMR and MDR, or serotype inclusion in vaccines. High-risk serotypes included serotypes 3, 19F, 19A, 14, 11A, 15A and serofamily 23. In addition, we present our data validating seroBA for in silico serotyping to facilitate ISO-accreditation of this test in routine use in a public health reference laboratory and have made this data set available. This study provides insights into the population dynamics, highlights non-vaccine serotypes of concern that are highly resistant, and provides a genomic framework for the ongoing surveillance of IPD in Australia which can inform next-generation IPD prevention strategies.
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    A statistical genomics framework to trace bacterial genomic predictors of clinical outcomes in Staphylococcus aureus bacteremia
    Giulieri, SG ; Guerillot, R ; Holmes, NE ; Baines, SL ; Hachani, A ; Hayes, AS ; Daniel, DS ; Seemann, T ; Davis, JS ; Van Hal, S ; Tong, SYC ; Stinear, TP ; Howden, BP (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.