ORCID Profile
0000-0002-0096-9474
Current Organisation
University of Melbourne at The Doherty Institute
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Publisher: Springer Science and Business Media LLC
Date: 09-08-2021
DOI: 10.1038/S41467-021-25073-W
Abstract: Salmonella enterica serovar 4,[5],12:i:- ( Salmonella 4,[5],12:i:-) is a monophasic variant of Salmonella Typhimurium that has emerged as a global cause of multidrug resistant salmonellosis. We used Bayesian phylodynamics, genomic epidemiology, and phenotypic characterization to describe the emergence and evolution of Salmonella 4,[5],12:i:- in Australia. We show that the interruption of the genetic region surrounding the phase II flagellin, FljB, causing a monophasic phenotype, represents a stepwise evolutionary event through the accumulation of mobile resistance elements with minimal impairment to bacterial fitness. We identify three lineages with different population dynamics and discrete antimicrobial resistance profiles emerged, likely reflecting differential antimicrobial selection pressures. Two lineages are associated with travel to South-East Asia and the third lineage is endemic to Australia. Moreover antimicrobial-resistant Salmonella 4,[5],12:i- lineages efficiently infected and survived in host phagocytes and epithelial cells without eliciting significant cellular cytotoxicity, suggesting a suppression of host immune response that may facilitate the persistence of Salmonella 4,[5],12:i:-.
Publisher: Elsevier BV
Date: 10-2021
Publisher: Springer Science and Business Media LLC
Date: 04-01-2023
DOI: 10.1038/S41467-022-35713-4
Abstract: 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.
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier BV
Date: 07-2023
Publisher: Cold Spring Harbor Laboratory
Date: 22-03-2023
DOI: 10.1101/2023.03.19.532693
Abstract: 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 revolutionised 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 maximises use of genomic variation data and is thus well-suited for supporting Legionnaires’ disease outbreak investigations.
Publisher: Cold Spring Harbor Laboratory
Date: 02-02-2023
DOI: 10.1101/2023.02.01.526694
Abstract: The capacity to undertake whole genome sequencing (WGS) in public health laboratories (PHLs) has grown rapidly in response to COVID-19, and SARS-CoV-2 genomic data has been invaluable for managing the pandemic. The public health response has been further supported by the rapid upgrade and implementation of laboratory and bioinformatic resources. However, there remains a high degree of variability in methods and capabilities between laboratories. In addition to evolving methodology and improved understanding of SARS-CoV-2, public health laboratories have become strained during surges in case numbers, adding to the difficulty of ensuring the highest data accuracy. Here, we formed a national working group comprised of laboratory scientists and bioinformaticians from Australia and New Zealand to improve data concordance across PHLs. Through investigating discordant sequence data from Australia’s first external SARS-CoV-2 WGS proficiency testing program (PTP), we show that most discrepancies in genome assessment arose from intrahost variation. While others could be remedied using reasonable, parsimonious bioinformatic quality control. Furthermore, we demonstrate how multidisciplinary national working groups can inform guidelines in real time for bioinformatic quality acceptance criteria. Provision of technical feedback allows laboratory improvement during a pandemic in real time, enhancing public health responses. The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. The COVID-19 pandemic has brought the utility of genomics to the forefront of microbial surveillance in public health. Our findings provide recommendations for monitoring bioinformatic quality controls in a pandemic context and how multidisciplinary national working groups can provide technical feedback for actionable improvement towards sequence data concordance.
Publisher: Microbiology Society
Date: 15-12-2021
Abstract: Non-typhoidal Salmonella (NTS) is the second most common cause of foodborne bacterial gastroenteritis in Australia with antimicrobial resistance (AMR) increasing in recent years. Whole-genome sequencing (WGS) provides opportunities for in silico detection of AMR determinants. The objectives of this study were two-fold: (1) establish the utility of WGS analyses for inferring phenotypic resistance in NTS, and (2) explore clinically relevant genotypic AMR profiles to third generation cephalosporins (3GC) in NTS lineages. The concordance of 2490 NTS isolates with matched WGS and phenotypic susceptibility data against 13 clinically relevant antimicrobials was explored. In silico serovar prediction and typing was performed on assembled reads and interrogated for known AMR determinants. The surrounding genomic context, plasmid determinants and co-occurring AMR patterns were further investigated for multidrug resistant serovars harbouring bla CMY-2 , bla CTX-M-55 or bla CTX-M-65 . Our data demonstrated a high correlation between WGS and phenotypic susceptibility testing. Phenotypic-genotypic concordance was observed between 2440/2490 (98.0 %) isolates, with overall sensitivity and specificity rates % and positive and negative predictive values %. The most common AMR determinants were bla TEM-1 , sul2 , tet (A), strA-strB and floR . Phenotypic resistance to cefotaxime and azithromycin was low and observed in 6.2 % (151/2486) and 0.9 % (16/1834) of the isolates, respectively. Several multi-drug resistant NTS lineages were resistant to 3GC due to different genetic mechanisms including bla CMY-2 , bla CTX-M-55 or bla CTX-M-65 . This study shows WGS can enhance existing AMR surveillance in NTS datasets routinely produced in public health laboratories to identify emerging AMR in NTS. These approaches will be critical for developing capacity to detect emerging public health threats such as resistance to 3GC.
Publisher: Microbiology Society
Date: 04-10-2023
Publisher: Elsevier BV
Date: 05-2022
Publisher: Microbiology Society
Date: 12-2020
Abstract: Complete genomes of microbial pathogens are essential for the phylogenomic analyses that increasingly underpin core public health laboratory activities. Here, we announce a BioProject (PRJNA556438) dedicated to sharing complete genomes chosen to represent a range of pathogenic bacteria with regional importance to Australia and the Southwest Pacific enriching the catalogue of globally available complete genomes for public health while providing valuable strains to regional public health microbiology laboratories. In this first step, we present 26 complete high-quality bacterial genomes. Additionally, we describe here a framework for reconstructing complete microbial genomes and highlight some of the challenges and considerations for accurate and reproducible genome reconstruction.
No related grants have been discovered for Susan Ballard.