Biology subjects

Howden, B.

Publications and source records attributed to Howden, B..

13 recordsLinked to original sources

bioRxiv2026

Incremental AMR acquisition driving successive genotype replacements and the rise of extensively drug resistant (XDR) Shigella sonnei in Australia over 20 years.

BackgroundIn Australia, the burden of shigellosis is predominantly in returning travellers or in men who have sex with men (MSM). Here, we combine genomic data with comprehensive epidemiological data on sexual exposure and international travel to explore population dynamics of Shigella sonnei and the expansion of multi-drug resistant (MDR) and extensively drug-resistant (XDR) sub-lineages. MethodsA population-level study of all cultured Shigella sonnei isolates in the state of Victoria, Australia, was undertaken between January 2002 and December 2024. Antimicrobial susceptibility testing, whole-genome sequencing, and bioinformatic analyses of 1,305 Shigella sonnei isolates were performed at the Microbiological Diagnostic Unit Public Health Laboratory. Enhanced metadata on source attribution including travel and sexual exposure were collected through surveillance forms or by interviews. ResultsThis study highlights significant shifts in Shigella sonnei cases in Victoria from sensitive strains to MDR and then XDR, particularly in the MSM-associated groups but also associated with a large point source outbreak. We describe an historical pattern of shifting genotype prevalence, and replacement to more varied and higher proportions of antimicrobial resistance over the last decade, resulting in the establishment of two distinct but highly concerning XDR sub-lineages within Victoria. ConclusionsOur genomic-epidemiological analyses highlight that drug-resistant Shigella sonnei remains an ongoing public health threat, and the importance of ongoing surveillance. We determined local evolutionary trajectories and identified expanding sub-lineages that informed shifts in clinical management and antimicrobial recommendations over time, including the use of azithromycin and carbapenems. Placing these local dynamics within the broader global epidemiology, we link how regional evolution interconnects with international dissemination, proving valuable context for guiding local, national and global strategies for prevent outbreaks and antimicrobial resistance.

microbiology↗

bioRxiv2025

Exploring use of ozone nanobubbles for removal of cyanobacteria and co-occurring antimicrobial resistance genes in water supply and reuse systems

Harmful cyanobacterial blooms present persistent risks to both drinking water security and wastewater reuse, driving the need for advanced treatment strategies. Treatment barrier(s) need to be capable of simultaneously controlling cyanobacteria, cyanotoxins, and co-occurring contaminants like bloom-associated antimicrobial resistance genes (ARGs) particularly in the case of recycling treated wastewater. Ozone nanobubble technology has emerged as a promising innovation, offering extended oxidative stability and enhanced interfacial reactivity compared to conventional ozonation. Hence this research objectives were to (a) assess the removal performance of ozone nanobubbles in eliminating cyanobacteria and their co-occurring contaminants in comparison to conventional ozone systems, and (b) investigate the repeatability of the results in varying background water qualities, ozone decay and the potential for by-products formation Ozonation using nanobubbles enhanced oxidation performance by 19%-34% in the drinking water reservoir compared to conventional ozonation while keeping the ozone concentration below 2mg/l. Lower oxidation efficiencies were observed in treated wastewater compared to drinking water sources, reflecting the higher content of organic matter and suspended solids, and oxidant demand characteristic of recycled water systems. Despite these challenges, ozone nanobubbles consistently outperformed conventional ozonation in reducing both cyanobacterial biomass and cell viability, underscoring their potential as an advanced "polishing" step for algal management in wastewater reuse applications. By exploring fate of ARGs alongside cyanobacteria and toxin removal, this work extends beyond traditional ozonation trials. It provides valuable field-based evidence that bridges the divide between laboratory efficacy and full-scale operational performance. Future studies should build on this by exploring combined or sequential treatment barriers that enhance DNA degradation, thereby addressing both cellular and genetic risks in water supply and reuse systems. Observing the action of nanobubbles under dynamic, real-world water quality conditions is currently challenging; however, this studys novel field trials demonstrate potential nanobubble applications and provide valuable insights to guide future investigations. The results reinforce the broader applicability of ozone nanobubble technology for multi-target contaminant control in water reservoirs.

microbiology↗

bioRxiv2025

A refined low-dose murine model of Mycobacterium ulcerans infection to assess integrated immune networks in Buruli ulcer pathogenesis

Mycobacterium ulcerans, the causative agent of Buruli ulcer, is a slow-growing zoonotic pathogen with distinctive pathogenesis linked primarily to its toxin mycolactone. Recent research has shown that the M. ulcerans infectious dose is very low (<10 colony forming units [CFU]). Buruli ulcer animal infection models traditionally use bacterial challenge doses in the range 104 - 106 CFU; a range orders of magnitude higher than natural infection. These large doses represent an unrealistic challenge for vaccine trials and studies of immunity. Here, we address this issue and describe a murine tail infection model in two genetically distinct mouse strains (BALB/c and C57BL/6) using quality-controlled, M. ulcerans challenge doses (10 - 20 CFU and 100 CFU). Over 24-weeks, we assessed host responses to infection by measuring >70 clinical, immunological and microbiological parameters. Principal findings included a 100% infection rate even at the lowest bacterial challenge, but with a dose-dependent delay in lesion onset and disease progression for both mouse strains. Bacterial growth kinetics were similar between mouse strains. There was a difference in immune profiles between mouse strains and between low (10 CFU) versus high (100 CFU) bacterial challenge doses. C57BL/6 mice exhibited more robust systemic cellular responses and more rapid lesion onset compared to BALB/c mice. There were dose-dependent cytokine and chemokine differences in C57BL/6 mice, while BALB/c mice displayed similar responses across both doses. Antibody responses were only detected late in the infection and were associated with the high-dose inoculum in both strains. Machine learning and other statistical analyses highlighted the importance of activated CD8+ T cells and dendritic cells in the immune response to low-dose infection in C57BL/6 mice. Murine low-dose M. ulcerans infection models provide confidence for future human Buruli ulcer challenge trials and will inform the development of effective vaccines and therapeutics.

microbiology↗

bioRxiv2025

Regulation of airway fumarate by host and pathogen promotes S. aureus pneumonia

Staphylococcus aureus is a leading cause of healthcare-associated pneumonia, contributing significantly to morbidity and mortality worldwide. As a ubiquitous colonizer of the upper respiratory tract, S. aureus must undergo substantial metabolic adaptation to achieve persistent infection in the distinctive microenvironment of the lung. We observed that fumC, which encodes the enzyme that converts fumarate to malate, is highly conserved with low mutation rates in S. aureus isolates from chronic lung infections. Fumarate, a pro-inflammatory metabolite produced by macrophages during infection, is regulated by the host fumarate hydratase (FH) to limit inflammation. Here, we demonstrate that fumarate, which accumulates in the chronically infected lung, is detrimental to S. aureus, blocking primary metabolic pathways such as glycolysis and oxidative phosphorylation (OXPHOS). This creates a metabolic bottleneck that drives staphylococcal FH (FumC) activity for airway adaptation. FumC not only degrades fumarate but also directs its utilization into critical pathways including the tricarboxylic acid (TCA) cycle, gluconeogenesis and hexosamine synthesis to maintain metabolic fitness and form a protective biofilm. Itaconate, another abundant immunometabolite in the infected airway enhances FumC activity, in synergy with fumarate. In a mouse model of pneumonia, a {Delta}fumC mutant displays significant attenuation compared to its parent and complemented strains, particularly in fumarate- and itaconate-replete conditions. Our findings underscore the pivotal role of immunometabolites in promoting S. aureus pulmonary adaptation.

microbiology↗

bioRxiv2024

Typhi Mykrobe: fast and accurate lineage identification and antimicrobial resistance genotyping directly from sequence reads for the typhoid fever agent Salmonella Typhi

BackgroundTyphoid fever results from systemic infection with Salmonella enterica serovar Typhi (Typhi) and causes 10 million illnesses annually. Disease control relies on prevention (water, sanitation, and hygiene interventions or vaccination) and effective antimicrobial treatment. Antimicrobial resistant (AMR) Typhi lineages have emerged and become established in many parts of the world. Knowledge of local pathogen populations informed by genomic surveillance, including of lineages (defined by the GenoTyphi scheme) and AMR determinants, is increasingly used to inform local treatment guidelines and to inform vaccination strategy. Current tools for genotyping Typhi require multiple read alignment or assembly steps and have not been validated for analysis of data generated with Oxford Nanopore Technologies (ONT) long-read sequencing devices. Here, we introduce Typhi Mykrobe, a command line software tool for rapid genotyping of Typhi lineages, AMR determinants, and plasmid replicons direct from sequencing reads. ResultsWe validated Typhi Mykrobe lineage genotyping by comparison with the current standard read mapping-based approach and demonstrated 99.8% concordance across nearly 13,000 genomes sequenced with Illumina platforms. For the few isolates with discordant calls, we show that Typhi Mykrobe results are better supported by the evidence from raw sequence read data than the results generated using the mapping-based approach. We also demonstrate 99.9% concordance for detection of AMR determinants compared with the current standard assembly-based approach, with similar results for plasmid marker detection. Typhi Mykrobe predicts clinical resistance categorisation (S/I/R) for eight drug classes, and we show strong agreement with phenotypic categorisations generated from reference laboratory minimum inhibitory concentration (MIC) data for n=1,572 Illumina-sequenced isolates (>99% agreement within one doubling dilution). We show strong concordance (>96% for genotype and >98% for AMR and plasmid) between calls made from ONT reads and those made from Illumina reads for isolates sequenced on both platforms (n =93 genomes). Typhi Mykrobe takes less than a minute per sample and is available at https://github.com/typhoidgenomics/genotyphi. ConclusionsTyphi Mykrobe provides rapid and sensitive genotyping of Typhi genomes direct from Illumina and ONT reads, although lower accuracy was observed for R9 ONT data. It demonstrated accurate assignment of GenoTyphi lineage, detection of AMR determinants and prediction of corresponding AMR phenotypes, and identification of plasmid replicons.

microbiology↗

bioRxiv2024

Diversity, functional classification and genotyping of SHV β-lactamases in Klebsiella pneumoniae

Interpreting phenotypes of blaSHV alleles in Klebsiella pneumoniae genomes is complex. While all strains are expected to carry a chromosomal copy conferring resistance to ampicillin, they may also carry mutations in chromosomal blaSHV alleles or additional plasmid-borne blaSHV alleles that have extended-spectrum {beta}-lactamase (ESBL) activity and/or {beta}-lactamase inhibitor (BLI) resistance activity. In addition, the role of individual mutations/amino acid changes is not completely documented or understood. This has led to confusion in the literature and in antimicrobial resistance (AMR) gene databases (e.g., NCBIs Reference Gene Catalog and the {beta}-lactamase database (BLDB)) over the specific functionality of individual SHV protein variants. Therefore, identification of ESBL-producing strains from K. pneumoniae genome data is complicated. Here, we reviewed the experimental evidence for the expansion of SHV enzyme function associated with specific amino-acid substitutions. We then systematically assigned SHV alleles to functional classes (wildtype, ESBL, BLI-resistant) based on the presence of these mutations. This resulted in the re-classification of 37 SHV alleles compared with current assignments in NCBIs Reference Gene Catalog and/or BLDB (21 to wildtype, 12 to ESBL, 4 to BLI-resistant). Phylogenetic and comparative genomic analyses support that; i) SHV-1 (encoded by blaSHV-1) is the ancestral chromosomal variant; ii) ESBL and BLI-resistant variants have evolved multiple times through parallel substitution mutations; iii) ESBL variants are mostly mobilised to plasmids; iv) BLI-resistant variants mostly result from mutations in chromosomal blaSHV. We used matched genome-phenotype data from the KlebNET-GSP Genotype-Phenotype Group to identify 3,999 K. pneumoniae isolates carrying one or more blaSHV alleles but no other acquired {beta}-lactamases, with which we assessed genotype-phenotype relationships for blaSHV. This collection includes human, animal, and environmental isolates collected between 2001 to 2021 from 24 countries across six continents. Our analysis supports that mutations at Ambler sites 238 and 179 confer ESBL activity, while most omega-loop substitutions do not. Our data also provide direct support for wildtype assignment of 67 protein variants, including eight that were noted in public databases as ESBL. We reclassified these eight variants as wildtype, because they lack ESBL-associated mutations, and our phenotype data support susceptibility to 3GCs (SHV-27, SHV-38, SHV-40, SHV-41, SHV-42, SHV-65, SHV-164, SHV-187). The approach and results outlined here have been implemented in Kleborate v2.4.1 (a software tool for genotyping K. pneumoniae from genome assemblies), whereby known and novel blaSHV alleles are classified based on causative mutations. Kleborate v2.4.1 was also updated to include ten novel protein variants from the KlebNET-GSP dataset and all alleles in public databases as of November 2023. This study demonstrates the power of sharing AMR phenotypes alongside genome data to improve understanding of resistance mechanisms. Impact statementSince every K. pneumoniae genome has an intrinsic SHV {beta}-lactamase and may also carry additional mobile forms, the correct interpretation of blaSHV genes detected in genome data can be challenging and can lead to K. pneumoniae being misclassified as ESBL-producing. Here, we use matched K. pneumoniae genome and drug susceptibility data contributed from dozens of studies, together with systematic literature review of experimental evidence, to improve our understanding of blaSHV allele variation and mapping of genotype to phenotype. This study shows the value of coordinated data sharing, in this case via the KlebNET-GSP Genotype-Phenotype Group, to improve our understanding of the evolutionary history and functionality of blaSHV genes. The results are captured in an open-source AMR dictionary utilised by the Kleborate genotyping tool, that could easily be incorporated into or used to update other tools and AMR gene databases. This work is part of the wider efforts of the KlebNET-GSP group to develop and support a unified platform tailored for the analysis and interpretation of K. pneumoniae genomes by a wide range of stakeholders. Data summaryBlaSHV allele sequences and class assignments are distributed with Kleborate, v2.4.1, DOI:10.5281/zenodo.10469001. Table S1 provides a summary of blaSHV alleles, including primary accessions, class-modifying mutations, and supporting evidence for class assignments that differ from NCBIs Reference Gene Catalog or BLDB. Whole genome sequence data are publicly available as reads and/or assemblies, individual accessions are given in Table S2; corresponding genotypes and antibiotic susceptibility phenotypes and measurements are available in Tables S3 and S4, respectively.

genomics↗

bioRxiv2024

Global diversity and evolution of Salmonella Panama, an understudied serovar causing gastrointestinal and invasive disease worldwide: a genomic epidemiology study

BackgroundNontyphoidal Salmonella (NTS) is a globally important bacterial pathogen, typically associated with foodborne gastrointestinal infection. Some NTS serovars can also colonise normally sterile sites in humans to cause invasive NTS (iNTS) disease. One understudied Salmonella enterica serovar which is responsible for a significant number of cases of iNTS disease is Panama. Despite global dissemination, numerous outbreaks, and a reported association with iNTS disease, S. enterica serovar Panama (S. Panama) has not been investigated in detail. MethodsUsing combined epidemiological and whole genome sequencing data we analysed 836 S. Panama genomes derived from historical collections, national surveillance datasets, and publicly available data. The collection represents all inhabited continents and includes isolates collected between 1931 and 2019. Maximum likelihood and Bayesian phylodynamic approaches were used to determine population structure & evolutionary history, and to infer geo-temporal dissemination. A combination of different bioinformatic approaches utilising short-read and long-read data were used to characterise geographic and clade-specific trends in antimicrobial resistance (AMR), and genetic markers for invasiveness. FindingsWe identified the presence of multiple geographically linked S. Panama clades, and regional trends in antimicrobial resistance profiles. Most isolates were pan-susceptible to antibiotics and belonged to clades circulating in the United States of America, Latin America, and the Caribbean. Multidrug resistant (MDR) isolates in our collection belonged to two phylogenetic clades circulating in Europe and Asia/Oceania, which exhibited the highest invasiveness indices based on the conservation of 196 extra-intestinal predictor genes. InterpretationThis first large-scale phylogenetic analysis of S. Panama revealed important information about population structure, AMR, global ecology, and genetic markers of invasiveness of the identified genomic subtypes. Our findings provide an important baseline for understanding S. Panama infection in the future. The presence of MDR clades with an elevated invasiveness index should be monitored by ongoing surveillance as such clades may pose an increased public health risk. FundingThe project was supported by both a Global Challenges Research Fund (GCRF) data & resources grant BBS/OS/GC/000009D and the BBSRC Core Capability Grant to the Earlham Institute BB/CCG1720/1. JCDH received a Wellcome Trust Investigator award (grant number 222528/Z/21/Z). CVP was supported by the John Lennon Memorial Scholarship from the University of Liverpool and a Fee Bursary Award from the Institute of Integrative Biology at the University of Liverpool. FXW was awarded the Institut Pasteur; Sante publique France; the Fondation Le Roch-Les Mousquetaires; and the French governments Investissement dAvenir programme, Laboratoire dExcellence "Integrative Biology of Emerging Infectious Diseases" (grant number ANR-10-LABX-62-IBEID). MDS was supported by a BBSRC grant (KSB: BB/V009184/1). RJBengtsson was supported by an MRC grant (KSB: MR/R020787/1). RJBennett was supported by a BBSRC DTP (BB/M011186/1). DJI was supported by a National Health and Medical Research Council (NHMRC) Emerging Leadership Fellowship (GNT1195210). BPH was supported by NHMRC Leadership Fellowship (GNT1196103). KSB and EVR are affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Gastrointestinal Infections at University of Liverpool in partnership with the United Kingdom Health Security Agency, in collaboration with University of Warwick. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health and Social Care or UKHSA. MAC is affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) (NIHR200892) in Genomics and Enabling Data at University of Warwick in partnership with the UK Health Security Agency (UKHSA), in collaboration with Universities of Cambridge and Oxford. MAC is based at UKHSA. The views expressed are those of the author(s) and not necessarily those of the NIHR, the Department of Health and Social Care or the UK Health Security Agency. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. No payment was received by any pharmaceutical company or other agency to write this article. The authors were not precluded from accessing data in the study, and accept responsibility to submit for publication. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSalmonella Panama has consistently been reported as a frequently isolated Salmonella serovar in national surveillance datasets, causing both sporadic cases and larger outbreaks. However, this picture has been masked due to most of the focus being placed on the top two serovars associated with nontyphoidal Salmonella (NTS) and invasive NTS disease: Typhimurium and Enteritidis. Previous works on S. Panama have determined transmission to include human faeces and breast milk, as well as non-human sources such as environmental reservoirs and animals (reptiles and pigs used for food). In contrast to most of the Salmonella serovars causing gastroenteritis, S. Panama has also been associated with a range of systemic infections including septicaemia, meningitis, and osteomyelitis, mostly affecting infants. The patterns of antimicrobial resistance of this pathogen were not well known, with studies reporting a mixture of different antimicrobial resistance profiles. Added value of this studyHere we conducted a large-scale study of 836 globally relevant S. Panama isolates to understand global population structure and disease ecology. The S. Panama genomes used in this study were sourced from a combination of historical collections (including the first ever isolated strain of this serovar dating from 1931), national surveillance datasets, and publicly available data. Some of these isolates had been linked to travel, allowing an inferred location for understanding population structure. Overall, this assembled S. Panama collection spanned a range of 88 years and covered 45 countries and regions of all inhabited continents. We applied bacterial phylodynamic approaches to determine population structure, evolutionary history, and clade-specific trends of invasiveness and antimicrobial resistance correlated with geo-temporal parameters. Implications of all the available evidenceThis study revealed the population structure and global ecology of S. Panama, indicating that multidrug resistant S. Panama circulates in European and Asian regions, and presents an increased number of genetic markers for extra-intestinal invasiveness. The transmission and expansion of antimicrobial-resistant S. Panama strains presented in this study highlight the significance of supporting control and monitoring efforts from an international perspective.

genomics↗

bioRxiv2023

Bringing TB genomics to the clinic: A comprehensive pipeline to predict antimicrobial susceptibility from genomic data, validated and accredited to ISO standards.

BackgroundWhole genome sequencing (WGS) is increasingly contributing to the clinical management of tuberculosis. Whilst the availability of bioinformatic tools for analysis and clinical reporting of Mycobacterium tuberculosis sequence data is improving, However, there remains a need for accessible, flexible bioinformatic tools that can be easily tailored for clinical reporting needs in different settings and are suitable for accreditation to international standards. MethodsWe developed tbtAMR, a flexible yet comprehensive tool for analysis of Mycobacterium tuberculosis genomic data, including inference of phenotypic susceptibility and lineage calling. Validation was undertaken using local and publicly-available real-world data (phenotype and genotype) and synthetic genomic data to determine the appropriate quality control metrics and extensively validate the pipeline for clinical use. FindingstbtAMR accurately predicted lineages and phenotypic susceptibility for first- and second-line drugs, with equivalent computational and predictive performance compared to other bioinformatics tools currently available. tbtAMR is flexible with modifiable criteria to tailor results to users needs. InterpretationThe tbtAMR tool is suitable for use in clinical and public health microbiology laboratory settings, and can be tailored to specific local needs by non-programmers. We have accredited this tool to ISO standards in our laboratory, and it has been implemented for routine reporting of AMR from genomic sequence data in a clinically relevant timeframe. Reporting templates, validation methods and datasets are provided to offer a pathway for laboratories to adopt and seek their own accreditation for this critical test, to improve the management of tuberculosis globally. FundingVictorian Government Department of Health; Australian National Health and Medical Research Council and Medical Research Futures Fund. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for studies using the search terms: "Mycobacterium tuberculosis", "clinical", "bioinformatics", "genomics", "drug resistance (OR antimicrobial)", published prior to 31st July 2024 without language restrictions (n=258). We considered all studies from this search that used genomics to infer likely drug-resistance in M. tuberculosis. Many of these studies highlight the challenges in making meaningful interpretations from genomic data for the purpose of inferring AMR for clinical applications. Despite the development of bioinformatics tools and compilation of catalogues of resistance conferring mutations, few of these studies directly address the challenges specific to implementation of a whole genome sequencing (WGS) and bioinformatics pipelines to deliver validated and accredited results for use in clinical applications and none provide sustainable solutions. Added value of this studyTo the best of our knowledge, this study is the first to detail practical solutions to challenges to validating and accrediting the routine inference of AMR from WGS data for clinical applications for treatment of M. tuberculosis. We developed, validated and accredited a bioinformatics tool, tbtAMR, which is robust and flexible, using a data-driven approach to inferring resistance, allowing for simple customisation of behaviour on a large collection of in-house data supplemented with publicly available data. Furthermore, we have made this pipeline and the accompanying data available for use by the wider public health community to aid others in implementing similar programs. Consultation with infectious disease clinicians, microbiologists and epidemiologists has allowed us to develop a robust workflow, that encompasses sequencing, analysis, interpretation, reporting, discrepancy resolution and ongoing verification. Implications of all the available evidenceThis study addressed the specific needs of reporting genomic AMR for M. tuberculosis in a clinical and public health laboratory. Furthermore, this work makes available a pipeline and data for laboratories to utilise to implement genomic AMR for M. tuberculosis. Through the use and further development of publicly available and open-source bioinformatics software, we have demonstrated the feasibility of clinical reporting of genomic AMR results for M. tuberculosis in a low-incidence high-income setting. We believe that this work lays the foundation for making reporting of genomic AMR for M. tuberculosis more accessible to public health and reference laboratories in general.

genomics↗

bioRxiv2023

High performance Legionella pneumophila source attribution using genomics-based machine learning classification

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.

bioinformatics↗

bioRxiv2022

Tracking the horizontal transfer of plasmids in Shigella sonnei and Shigella flexneri using phylogenetics

The silent pandemic of antimicrobial resistance (AMR) represents a significant global public health threat. AMR genes in bacteria are often carried on mobile elements, such as plasmids. The horizontal movement of plasmids allows AMR genes and resistance to key therapeutics to disseminate in a population. However, the quantification of the movement of plasmids remains challenging with existing computational approaches. Here, we introduce a novel method that allows us to reconstruct and quantify the movement of plasmids in bacterial populations over time. To do so, we model chromosomal and plasmid DNA co-evolution using a joint coalescent and plasmid transfer process in a Bayesian phylogenetic network approach. This approach reconstructs differences in the evolutionary history of plasmids and chromosomes to reconstruct instances where plasmids likely move between bacterial lineages while accounting for parameter uncertainty. We apply this new approach to a five-year dataset of Shigella, exploring the plasmid transfer rates of five different plasmids with different AMR and virulence profiles. In doing so, we reconstruct the co-evolution of the large Shigella virulence plasmid with the chromosome DNA. We quantify higher plasmid transfer rates of three small plasmids that move between lineages of Shigella sonnei. Finally, we determine the recent dissemination of a multidrug-resistant plasmid between S. sonnei and S. flexneri lineages in multiple independent events and through steady growth in prevalence since 2010. This approach has a strong potential to improve our understanding of the evolutionary dynamics of AMR-carrying plasmids as they are introduced, circulate, and are maintained in bacterial populations.

evolutionary biology↗

bioRxiv2021

Significant Variability exists in the Toxicity of Global Methicillin-resistant Staphylococcus aureus Lineages.

Staphylococcus aureus is a major human pathogen where the emergence of antibiotic resistant lineages, such as methicillin-resistant S. aureus (MRSA), is a major health concern. While some MRSA lineages are restricted to the healthcare setting, the epidemiology of MRSA is changing globally, with the rise of specific lineages causing disease in healthy people in the community. In the past two decades, community-associated MRSA (CA-MRSA) has emerged as a clinically important and virulent pathogen associated with serious skin and soft-tissue infections (SSTI). These infections are primarily toxin driven, leading to the suggestion that hyper-virulent lineages/multi-locus sequence types (STs) exist. To examine this, we compared the toxic activity of 475 MRSA isolates representing five major MRSA STs (ST22, ST93, ST8, ST239 and ST36) by employing a monocyte-macrophage THP-1 cell line as a surrogate for measuring gross cytotoxicity. We demonstrate that while certain MRSA STs contain highly toxic isolates, there is such variability within lineages to suggest that this aspect of virulence should not be inferred from the genotype of any given isolate. Furthermore, by interrogating the accessory gene regulator (Agr) sequences in this collection we identified several Agr mutations that were associated with reduced toxicity. Interestingly, the majority of isolates that were attenuated in toxin production contained no mutations in the agr locus, indicating a role of other undefined genes in S. aureus toxin regulation.

microbiology↗

bioRxiv2020

Systematic analysis of key parameters for genomics-based real-time detection and tracking of multidrug-resistant bacteria

BackgroundPairwise single nucleotide polymorphisms (SNPs) are a cornerstone for genomic approaches to multidrug-resistant organisms (MDROs) transmission inference in hospitals. However, the impact of key analysis parameters on these inferences has not been systematically analysed. MethodsWe conducted a multi-hospital 15-month prospective study, sequencing 1537 MDRO genomes for comparison; methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus faecium, and extended-spectrum beta-lactamase-producing Escherichia coli and Klebsiella pneumoniae. We systematically assessed the impact of sample and reference genome diversity, masking of prophage and regions of recombination, cumulative genome analysis compared to a three-month sliding-window, and the comparative effects each of these had when applying a SNP threshold for inferring likely transmission ([≤]15 SNPs for S. aureus, [≤]25 for other species). FindingsAcross the species, using a reference genome of the same sequence type provided a greater degree of pairwise SNP resolution, compared to species and outgroup-reference alignments that typically resulted in inflated SNP distances and the possibility of missed transmission events. Omitting prophage regions had minimal impacts, however, omitting recombination regions a highly variable effect, often inflating the number of closely related pairs. Estimating pairwise SNP distances was more consistent using a sliding-window than a cumulative approach. InterpretationThe use of a closely-related reference genome, without masking of prophage or recombination regions, and a sliding-window for isolate inclusion is best for accurate and consistent MDRO transmission inference. The increased stability and resolution provided by these approaches means SNP thresholds for putative transmission inference can be more reliably applied among diverse MDROs. FundingThis work was supported by the Melbourne Genomics Health Alliance (funded by the State Government of Victoria, Department of Health and Human Services, and the ten member organizations); an National Health and Medical Research Council (Australia) Partnership grant (GNT1149991) and individual grants from National Health and Medical Research Council (Australia) to NLS (GNT1093468), JCK (GNT1008549) and BPH (GNT1105905).

genomics↗

bioRxiv2017

A Supervised Statistical Learning Approach For Accurate Legionella pneumophila Source Attribution During Outbreaks

Public health agencies are increasingly relying on genomics during Legionnaires disease investigations. However, the causative bacterium (Legionella pneumophila) has an unusual population structure with extreme temporal and spatial genome sequence conservation. Furthermore, Legionnaires disease outbreaks can be caused by multiple L. pneumophila genotypes in a single source. These factors can confound cluster identification using standard phylogenomic methods. Here, we show that a statistical learning approach based on\n\nL. pneumophila core genome single nucleotide polymorphism (SNP) comparisons eliminates ambiguity for defining outbreak clusters and accurately predicts exposure sources for clinical cases. We illustrate the performance of our method by genome comparisons of 234 L. pneumophila isolates obtained from patients and cooling towers in Melbourne, Australia between 1994 and 2014. This collection included one of the largest reported Legionnaires disease outbreaks, involving 125 cases at an aquarium. Using only sequence data from L. pneumophila cooling tower isolates and including all core genome variation, we built a multivariate model using discriminant analysis of principal components (DAPC) to find cooling tower-specific genomic signatures, and then used it to predict the origin of clinical isolates. Model assignments were 93% congruent with epidemiological data, including the aquarium Legionnaires outbreak and three other unrelated outbreak investigations. We applied the same approach to a recently described investigation of Legionnaires disease within a UK hospital and observed model predictive ability of 86%. We have developed a promising means to breach L. pneumophila genetic diversity extremes and provide objective source attribution data for outbreak investigations.

microbiology↗