The epidemiology of enterococci in a tertiary hospital and primary healthcare facilities in Fiji (2019-2022).

OBJECTIVES: We analysed 4 y of laboratory data to characterise the species and determine the antimicrobial susceptibility profiles of enterococci as human pathogens in Fiji. The study also investigated the molecular epidemiology amongst the subset of vancomycin-resistant enterococci (VRE). METHODS: This retrospective study reviewed bacteriological data from Colonial War Memorial Hospital (CWMH) and other healthcare facilities in the Central and Eastern divisions of Fiji. Phenotypic, antimicrobial susceptibility and vanA and vanB PCR testing were performed using locally approved protocols. The first clinical isolates per patient with antimicrobial susceptibility testing results in a single year were included in the analysis. Data was analysed using WHONET software and Microsoft Excel. RESULTS: A total of 1817 enterococcal isolates were reported, 1415 from CWMH and 402 from other healthcare facilities. The majority of isolates, 75% (n = 1362) were reported as undifferentiated Enterococcus spp., 17.8% (n = 324) were specifically identified as Enterococcus faecalis and 6.7% (n = 122) as E. faecium. Overall, 10% of the enterococci isolates were from blood cultures. Among isolates from CWMH, <15% of E. faecium were susceptible to ampicillin, and 17.2% were vancomycin resistant. Overall, 874 enterococcal isolates (including the undifferentiated species) were tested against vancomycin, of which 4.8% (n = 42) were resistance. All of the VRE isolates tested (n = 15) expressed vanA genes. CONCLUSIONS: This study demonstrates the clinical importance of VRE, particularly van A E. faecium in the national referral hospital in Fiji. Enhanced phenotypic and molecular surveillance data are needed to better understand enterococci epidemiology and help guide specific infection prevention and control measures and antibiotic prescribing guidelines.

The changing epidemiology of antimicrobial resistance in Fiji: a descriptive analysis of antimicrobial susceptibility and trends of endemic and emerging pathogens, 2019-2022.

Background: There is a paucity of data on antimicrobial resistance in Fiji. The aim of this study was to determine the antimicrobial susceptibility profile of bacterial isolates from clinical samples at Colonial War Memorial Hospital in Fiji. Methods: This retrospective study reviewed four-year of data from January 1, 2019, through December 31, 2022. Laboratory testing was carried out using locally approved protocols. Selective antimicrobial susceptibility testing was performed whereby only isolates resistant to first line antimicrobials were tested against second line antimicrobials. Only the first isolate of a given species per patient in a single year were included in the analysis. WHONET software and Microsoft Excel were used for analysis. Findings: A total of 29,222 bacterial isolates were included, 62% (n = 18,084) were Gram-negative bacteria. K. pneumoniae was the most common (n = 5363), followed by E. coli (n = 4321). Extended spectrum beta lactamase (ESBL) production increased from 30% in 2019 to 43% in 2022 amongst K. pneumoniae, and 10%-23% in E coli. There were 733 carbapenem-resistant isolates identified from clinical samples, 61% (n = 445) were A. baumannii, 15% (n = 110) E. coli and 14% (n = 101) P. aeruginosa. Amongst the E. coli isolates tested, susceptibility to meropenem declined from 99% (272/274) in 2019 to 79% (255/325) in 2022. The rate of methicillin resistance amongst Staphylococcus aureus was steady, remaining between 11% and 13%. Interpretation: This study demonstrated a high rate of MDR amongst Gram-negative bacteria, especially ESBL producing K. pneumoniae and E. coli and carbapenem-resistant A. baumannii. The emergence and rapid spread of carbapenemase producing E. coli in Fiji's largest hospital is of particular concern. There is an urgent need to allocate resources to improve existing capacity and to develop effective multimodal strategies to detect, manage and control the spread of MDR organisms. Funding: This study was supported by the Medical Research Future Fund through the Australian government (grant number APP 1200970).

Emergence and clonal expansion of a qacA-harbouring sequence type 45 lineage of methicillin-resistant Staphylococcus aureus.

The past decade has seen an increase in the prevalence of sequence type (ST) 45 methicillin-resistant Staphylococcus aureus (MRSA), yet the underlying drivers for its emergence and spread remain unclear. To better understand the worldwide dissemination of ST45 S. aureus, we performed phylogenetic analyses of Australian isolates, supplemented with a global population of ST45 S. aureus genomes. Our analyses revealed a distinct lineage of multidrug-resistant ST45 MRSA harbouring qacA, predominantly found in Australia and Singapore. Bayesian inference predicted that the acquisition of qacA occurred in the late 1990s. qacA was integrated into a structurally variable region of the chromosome containing Tn552 (carrying blaZ) and Tn4001 (carrying aac(6')-aph(2")) transposable elements. Using mutagenesis and in vitro assays, we provide phenotypic evidence that qacA confers tolerance to chlorhexidine. These findings collectively suggest both antimicrobial resistance and the carriage of qacA may play a role in the successful establishment of ST45 MRSA.

Phage resistance in Klebsiella pneumoniae and bidirectional effects impacting antibiotic susceptibility.

OBJECTIVES: Bacteriophage (phage) therapy is a promising anti-infective option to combat antimicrobial resistance. However, the clinical utilization of phage therapy has been severely compromised by the potential emergence of phage resistance. Although certain phage resistance mechanisms can restore bacterial susceptibility to certain antibiotics, a lack of knowledge of phage resistance mechanisms hinders optimal use of phages and their combination with antibiotics. METHODS: Genome-wide transposon screening was performed with a mutant library of Klebsiella pneumoniae MKP103 to identify phage pKMKP103_1-resistant mutants. Phage-resistant phenotypes were evaluated by time-kill kinetics and efficiency of plating assays. Phage resistance mechanisms were investigated with adsorption, one-step growth, and mutation frequency assays. Antibiotic susceptibility was determined with broth microdilution and population analysis profiles. RESULTS: We observed a repertoire of phage resistance mechanisms in K pneumoniae, such as disruption of phage binding (fhuA::Tn and tonB::Tn), extension of the phage latent period (mnmE::Tn and rpoN::Tn), and increased mutation frequency (mutS::Tn and mutL::Tn). Notably, in contrast to the prevailing view that phage resistance re-sensitizes antibiotic-resistant bacteria, we observed a bidirectional steering effect on bacterial antibiotic susceptibility. Specifically, rpoN::Tn increased susceptibility to colistin while mutS::Tn and mutL::Tn increased resistance to rifampicin and colistin. DISCUSSION: Our findings demonstrate that K pneumoniae employs multiple strategies to overcome phage infection, which may result in enhanced or reduced antibiotic susceptibility. Mechanism-guided phage steering should be incorporated into phage therapy to better inform clinical decisions on phage-antibiotic combinations.

Development of a cross-sectoral antimicrobial resistance capability assessment framework.

Antimicrobial resistance (AMR) is an urgent and growing global health concern, and a clear understanding of existing capacities to address AMR, particularly in low-income and middle-income countries (LMICs), is needed to inform national priorities, investment targets and development activities. Across LMICs, there are limited data regarding existing mechanisms to address AMR, including national AMR policies, current infection prevention and antimicrobial prescribing practices, antimicrobial use in animals, and microbiological testing capacity for AMR. Despite the development of numerous individual tools designed to inform policy formulation and implementation or surveillance interventions to address AMR, there is an unmet need for easy-to-use instruments that together provide a detailed overview of AMR policy, practice and capacity. This paper describes the development of a framework comprising five assessment tools which provide a detailed assessment of country capacity to address AMR within both the human and animal health sectors. The framework is flexible to meet the needs of implementers, as tools can be used separately to assess the capacity of individual institutions or as a whole to align priority-setting and capacity-building with AMR National Action Plans (NAPs) or national policies. Development of the tools was conducted by a multidisciplinary team across three phases: (1) review of existing tools; (2) adaptation of existing tools; and (3) piloting, refinement and finalisation. The framework may be best used by projects which aim to build capacity and foster cross-sectoral collaborations towards the surveillance of AMR, and by LMICs wishing to conduct their own assessments to better understand capacity and capabilities to inform future investments or the implementation of NAPs for AMR.

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 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.

Key Challenges for Respiratory Virus Surveillance while Transitioning out of Acute Phase of COVID-19 Pandemic.

To support the ongoing management of viral respiratory diseases while transitioning out of the acute phase of the COVID-19 pandemic, many countries are moving toward an integrated model of surveillance for SARS-CoV-2, influenza virus, and other respiratory pathogens. Although many surveillance approaches catalyzed by the COVID-19 pandemic provide novel epidemiologic insight, continuing them as implemented during the pandemic is unlikely to be feasible for nonemergency surveillance, and many have already been scaled back. Furthermore, given anticipated cocirculation of SARS-CoV-2 and influenza virus, surveillance activities in place before the pandemic require review and adjustment to ensure their ongoing value for public health. In this report, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies as well as their contribution to epidemiologic assessment, forecasting, and public health decision-making.

Whole genome sequencing of drug-resistant Mycobacterium tuberculosis isolates in Victoria, Australia.

OBJECTIVES: Whole genome sequencing (WGS) can identify clusters, transmission patterns, and drug resistance mutations. This is important in low-burden settings such as Australia, as it can assist in efficient contact tracing and surveillance. METHODS: We conducted a retrospective cohort study using WGS from 155 genomically defined drug-resistant Mycobacterium tuberculosis (DR-TB) isolates collected between 2018-2021 in Victoria, Australia. Bioinformatic analysis was performed to identify resistance-conferring mutations, lineages, clusters and understand how local sequences compared with international context. RESULTS: Of the 155 sequences, 42% were identified as lineage 2 and 35% as lineage 1; 65.8% (102/155) were isoniazid mono-resistant, 8.4% were multi-drug resistant TB and 5.8% were pre-extensively drug-resistant / extensively drug-resistant TB. The most common mutations were observed in katG and fabG1 genes, especially at Ser315Thr and fabG1 -15 C>T for first-line drugs. Ser450Leu was the most frequent mutation in rpoB gene. Phylogenetic analysis confirmed that Victorian DR-TB were associated with importation events. There was little evidence of local transmission with only five isolate pairs. CONCLUSION: Isoniazid-resistant TB is the commonest DR-TB in Victoria, and the mutation profile is similar to global circulating DR-TB. Most cases are diagnosed among migrants with limited transmission. This study highlights the value of WGS in identification of clusters and resistance-conferring mutations. This information is crucial in supporting disease mitigation and treatment strategies.

High-performance enrichment-based genome sequencing to support the investigation of hepatitis A virus outbreaks.

IMPORTANCE: 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.

Genomic surveillance for antimicrobial resistance - a One Health perspective.

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.

Genomics for public health and international surveillance of antimicrobial resistance.

Historically, epidemiological investigation and surveillance for bacterial antimicrobial resistance (AMR) has relied on low-resolution isolate-based phenotypic analyses undertaken at local and national reference laboratories. Genomic sequencing has the potential to provide a far more high-resolution picture of AMR evolution and transmission, and is already beginning to revolutionise how public health surveillance networks monitor and tackle bacterial AMR. However, the routine integration of genomics in surveillance pipelines still has considerable barriers to overcome. In 2022, a workshop series and online consultation brought together international experts in AMR and pathogen genomics to assess the status of genomic applications for AMR surveillance in a range of settings. Here we focus on discussions around the use of genomics for public health and international AMR surveillance, noting the potential advantages of, and barriers to, implementation, and proposing recommendations from the working group to help to drive the adoption of genomics in public health AMR surveillance. These recommendations include the need to build capacity for genome sequencing and analysis, harmonising and standardising surveillance systems, developing equitable data sharing and governance frameworks, and strengthening interactions and relationships among stakeholders at multiple levels.

Evidence review and recommendations for the implementation of genomics for antimicrobial resistance surveillance: reports from an international expert group.

Nearly a century after the beginning of the antibiotic era, which has been associated with unparalleled improvements in human health and reductions in mortality associated with infection, the dwindling pipeline for new antibiotic classes coupled with the inevitable spread of antimicrobial resistance (AMR) poses a major global challenge. Historically, surveillance of bacteria with AMR typically relied on phenotypic analysis of isolates taken from infected individuals, which provides only a low-resolution view of the epidemiology behind an individual infection or wider outbreak. Recent years have seen increasing adoption of powerful new genomic technologies with the potential to revolutionise AMR surveillance by providing a high-resolution picture of the AMR profile of the bacteria causing infections and providing real-time actionable information for treating and preventing infection. However, many barriers remain to be overcome before genomic technologies can be adopted as a standard part of routine AMR surveillance around the world. Accordingly, the Surveillance and Epidemiology of Drug-resistant Infections Consortium convened an expert working group to assess the benefits and challenges of using genomics for AMR surveillance. In this Series, we detail these discussions and provide recommendations from the working group that can help to realise the massive potential benefits for genomics in surveillance of AMR.

Bioinformatic investigation of discordant sequence data for SARS-CoV-2: insights for robust genomic analysis during pandemic surveillance.

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.

Comparison of contemporary invasive and non-invasive Streptococcus pneumoniae isolates reveals new insights into circulating anti-microbial resistance determinants.

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.

The two-component system WalKR provides an essential link between cell wall homeostasis and DNA replication in Staphylococcus aureus.

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.

Clinical Staphylococcus aureus inhibits human T-cell activity through interaction with the PD-1 receptor.

IMPORTANCE: 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.

Cryptic susceptibility to penicillin/ß-lactamase inhibitor combinations in emerging multidrug-resistant, hospital-adapted Staphylococcus epidermidis lineages.

Global spread of multidrug-resistant, hospital-adapted Staphylococcus epidermidis lineages underscores the need for new therapeutic strategies. Here we show that many S. epidermidis isolates belonging to these lineages display cryptic susceptibility to penicillin/ß-lactamase inhibitor combinations under in vitro conditions, despite carrying the methicillin resistance gene mecA. Using a mouse thigh model of S. epidermidis infection, we demonstrate that single-dose treatment with amoxicillin/clavulanic acid significantly reduces methicillin-resistant S. epidermidis loads without leading to detectable resistance development. On the other hand, we also show that methicillin-resistant S. epidermidis is capable of developing increased resistance to amoxicillin/clavulanic acid during long-term in vitro exposure to these drugs. These findings suggest that penicillin/ß-lactamase inhibitor combinations could be a promising therapeutic candidate for treatment of a high proportion of methicillin-resistant S. epidermidis infections, although the in vivo risk of resistance development needs to be further addressed before they can be incorporated into clinical trials.

A statistical genomics framework to trace bacterial genomic predictors of clinical outcomes in Staphylococcus aureus bacteremia.

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.

Global diversity and antimicrobial resistance of typhoid fever pathogens: Insights from a meta-analysis of 13,000 Salmonella Typhi genomes.

Background: The Global Typhoid Genomics Consortium was established to bring together the typhoid research community to aggregate and analyse Salmonella enterica serovar Typhi (Typhi) genomic data to inform public health action. This analysis, which marks 22 years since the publication of the first Typhi genome, represents the largest Typhi genome sequence collection to date (n=13,000). Methods: This is a meta-analysis of global genotype and antimicrobial resistance (AMR) determinants extracted from previously sequenced genome data and analysed using consistent methods implemented in open analysis platforms GenoTyphi and Pathogenwatch. Results: Compared with previous global snapshots, the data highlight that genotype 4.3.1 (H58) has not spread beyond Asia and Eastern/Southern Africa; in other regions, distinct genotypes dominate and have independently evolved AMR. Data gaps remain in many parts of the world, and we show the potential of travel-associated sequences to provide informal 'sentinel' surveillance for such locations. The data indicate that ciprofloxacin non-susceptibility (>1 resistance determinant) is widespread across geographies and genotypes, with high-level ciprofloxacin resistance (≥3 determinants) reaching 20% prevalence in South Asia. Extensively drug-resistant (XDR) typhoid has become dominant in Pakistan (70% in 2020) but has not yet become established elsewhere. Ceftriaxone resistance has emerged in eight non-XDR genotypes, including a ciprofloxacin-resistant lineage (4.3.1.2.1) in India. Azithromycin resistance mutations were detected at low prevalence in South Asia, including in two common ciprofloxacin-resistant genotypes. Conclusions: The consortium's aim is to encourage continued data sharing and collaboration to monitor the emergence and global spread of AMR Typhi, and to inform decision-making around the introduction of typhoid conjugate vaccines (TCVs) and other prevention and control strategies. Funding: No specific funding was awarded for this meta-analysis. Coordinators were supported by fellowships from the European Union (ZAD received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 845681), the Wellcome Trust (SB, Wellcome Trust Senior Fellowship), and the National Health and Medical Research Council (DJI is supported by an NHMRC Investigator Grant [GNT1195210]).

Salmonella Typhi (Typhi) is a type of bacteria that causes typhoid fever. More than 110,000 people die from this disease each year, predominantly in areas of sub-Saharan Africa and South Asia with limited access to safe water and sanitation. Clinicians use antibiotics to treat typhoid fever, but scientists worry that the spread of antimicrobial-resistant Typhi could render the drugs ineffective, leading to increased typhoid fever mortality. The World Health Organization has prequalified two vaccines that are highly effective in preventing typhoid fever and may also help limit the emergence and spread of resistant Typhi. In low resource settings, public health officials must make difficult trade-off decisions about which new vaccines to introduce into already crowded immunization schedules. Understanding the local burden of antimicrobial-resistant Typhi and how it is spreading could help inform their actions. The Global Typhoid Genomics Consortium analyzed 13,000 Typhi genomes from 110 countries to provide a global overview of genetic diversity and antimicrobial-resistant patterns. The analysis showed great genetic diversity of the different strains between countries and regions. For example, the H58 Typhi variant, which is often drug-resistant, has spread rapidly through Asia and Eastern and Southern Africa, but is less common in other regions. However, distinct strains of other drug-resistant Typhi have emerged in other parts of the world. Resistance to the antibiotic ciprofloxacin was widespread and accounted for over 85% of cases in South Africa. Around 70% of Typhi from Pakistan were extensively drug-resistant in 2020, but these hard-to-treat variants have not yet become established elsewhere. Variants that are resistant to both ciprofloxacin and ceftriaxone have been identified, and azithromycin resistance has also appeared in several different variants across South Asia. The Consortium's analyses provide valuable insights into the global distribution and transmission patterns of drug-resistant Typhi. Limited genetic data were available fromseveral regions, but data from travel-associated cases helped fill some regional gaps. These findings may help serve as a starting point for collective sharing and analyses of genetic data to inform local public health action. Funders need to provide ongoing supportto help fill global surveillance data gaps.

The importance of utilizing travel history metadata for informative phylogeographical inferences: a case study of early SARS-CoV-2 introductions into Australia.

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.