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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.Antimicrobial resistance (AMR) is an important public health issue that affects human, animal and environmental sectors worldwide. The authors review the role of genomics in AMR surveillance using a One Health approach, and how genomic approaches can help mitigate the spread of AMR to improve global health.

Genomic sequencing has significant potential to inform public health management for SARS-CoV-2. Here we report high-throughput genomics for SARS-CoV-2, sequencing 80% of cases in Victoria, Australia (population 6.24 million) between 6 January and 14 April 2020 (total 1,333 COVID-19 cases). We integrate epidemiological, genomic and phylodynamic data to identify clusters and impact of interventions. The global diversity of SARS-CoV-2 is represented, consistent with multiple importations. Seventy-six distinct genomic clusters were identified, including large clusters associated with social venues, healthcare and cruise ships. Sequencing sequential samples from 98 patients reveals minimal intra-patient SARS-CoV-2 genomic diversity. Phylodynamic modelling indicates a significant reduction in the effective viral reproductive number ( R e ) from 1.63 to 0.48 after implementing travel restrictions and physical distancing. Our data provide a concrete framework for the use of SARS-CoV-2 genomics in public health responses, including its use to rapidly identify SARS-CoV-2 transmission chains, increasingly important as social restrictions ease globally. Genome sequencing can be used to infer pathogen transmission dynamics and inform public health responses. Here, the authors sequence >1,200 SARS-CoV-2 samples from Victoria, Australia and find genomic support for the effectiveness of social restrictions in reducing transmission.

The COVID-19 pandemic has driven demand for integrated genomics, resulting in fast-tracked development of AusTrakka, Australia’s pathogen genomics platform. This facilitated rapid data sharing, democratised access to computational and bioinformatic resources and expertise, and achieved national real-time genomic surveillance. The COVID-19 pandemic has accelerated the demand for near real-time analysis and dissemination of pathogen genomic data. In this comment, the authors describe how Australia has developed and rolled out its SARS-CoV-2 genomics platform, AusTrakka, and used it to support public health action.

Vancomycin-resistant Enterococcus faecium (VREfm) is a major nosocomial pathogen. Identifying VREfm transmission dynamics permits targeted interventions, and while genomics is increasingly being utilised, methods are not yet standardised or optimised for accuracy. We aimed to develop a standardized genomic method for identifying putative VREfm transmission links. Using comprehensive genomic and epidemiological data from a cohort of 308 VREfm infection or colonization cases, we compared multiple approaches for quantifying genetic relatedness. We showed that clustering by core genome multilocus sequence type (cgMLST) was more informative of population structure than traditional MLST. Pairwise genome comparisons using split k-mer analysis (SKA) provided the high-level resolution needed to infer patient-to-patient transmission. The more common mapping to a reference genome was not sufficiently discriminatory, defining more than three times more genomic transmission events than SKA (3729 compared to 1079 events). Here, we show a standardized genomic framework for inferring VREfm transmission that can be the basis for global deployment of VREfm genomics into routine outbreak detection and investigation. Vancomycin-resistant Enterococcus faecium is an important healthcare-associated pathogen and genomic analyses could inform targeted interventions. Here, the authors optimise an analysis pipeline for identification of putative transmission events using core genome multilocus sequence type clustering and split kmer analysis.

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. The implementation of genomics for identification and surveillance of antimicrobial resistance (AMR) in clinical laboratories remains challenging. Here, Sherry et al. present a bioinformatics platform for detection of AMR determinants from whole-genome sequencing data, suitable for clinical and public-health microbiology reporting.

Variant calling is fundamental in bacterial genomics, underpinning the identification of disease transmission clusters, the construction of phylogenetic trees, and antimicrobial resistance detection. This study presents a comprehensive benchmarking of variant calling accuracy in bacterial genomes using Oxford Nanopore Technologies (ONT) sequencing data. We evaluated three ONT basecalling models and both simplex (single-strand) and duplex (dual-strand) read types across 14 diverse bacterial species. Our findings reveal that deep learning-based variant callers, particularly Clair3 and DeepVariant, significantly outperform traditional methods and even exceed the accuracy of Illumina sequencing, especially when applied to ONT’s super-high accuracy model. ONT’s superior performance is attributed to its ability to overcome Illumina’s errors, which often arise from difficulties in aligning reads in repetitive and variant-dense genomic regions. Moreover, the use of high-performing variant callers with ONT’s super-high accuracy data mitigates ONT’s traditional errors in homopolymers. We also investigated the impact of read depth on variant calling, demonstrating that 10× depth of ONT super-accuracy data can achieve precision and recall comparable to, or better than, full-depth Illumina sequencing. These results underscore the potential of ONT sequencing, combined with advanced variant calling algorithms, to replace traditional short-read sequencing methods in bacterial genomics, particularly in resource-limited settings. Imagine being part of a public health institution when, suddenly, cases of Salmonella surge across your country. You are facing an outbreak of this foodborne disease, and the clock is ticking. People are consuming a contaminated product that is making them sick; how do you identify related cases, track the source of the infection, and shut down its production? In situations like these, scientists need to tell apart even closely related strains of the same bacterial species. This process, known as variant calling, relies on first analysing (or ‘sequencing’) the genetic information obtained from the bacteria of interest, then comparing it to a reference genome. Currently, two main approaches are available for genome sequencing. Traditional ‘short-read’ technologies tend to be more accurate but less reliable when covering certain types of genomic regions. New ‘long-read’ approaches can bypass these limitations though they have historically been less accurate. Comparison with a reference genome can be performed using a tool known as a variant caller. Many of the most effective ones are now based on artificial intelligence approaches such as deep learning. However, these have primarily been applied to human genomic data so far; it therefore remains unclear whether they are equally useful for bacterial genomes. In response, Hall et al. set out to investigate the accuracy of four deep learning-based and three traditional variant callers on datasets from 14 bacterial species obtained via long-read approaches. Their respective performance was also benchmarked against a more conventional approach representing a standard of accuracy (that is, a popular, non-deep learning variant caller used on short-read datasets). These analyses were performed on a ‘truthset’ established by Hall et al., a collection of validated data that allowed them to assess the performance of the various tools tested. The results show that, in this context, the deep learning variant callers more accurately detected genetic variations compared to the traditional approach. These tools, which could be run on standard laptops, were effective even with low amounts of sequencing data – making them useful even in settings where resources are limited. Variant calling is an essential step in tracking the emergence and spread of disease, identifying new strains of bacteria, and examining their evolution. The findings by Hall et al. should therefore benefit various sectors, particularly clinical and public health laboratories.

Horizontal gene transfer (HGT) is a major driving force of bacterial diversification and evolution. For tuberculosis-causing mycobacteria, the impact of HGT in the emergence and distribution of dominant lineages remains a matter of debate. Here, by using fluorescence-assisted mating assays and whole genome sequencing, we present unique experimental evidence of chromosomal DNA transfer between tubercle bacilli of the early-branching Mycobacterium canettii clade. We found that the obtained recombinants had received multiple donor-derived DNA fragments in the size range of 100 bp to 118 kbp, fragments large enough to contain whole operons. Although the transfer frequency between M. canettii strains was low and no transfer could be observed among classical Mycobacterium tuberculosis complex (MTBC) strains, our study provides the proof of concept for genetic exchange in tubercle bacilli. This outstanding, now experimentally validated phenomenon presumably played a key role in the early evolution of the MTBC toward pathogenicity. Moreover, our findings also provide important information for the risk evaluation of potential transfer of drug resistance and fitness mutations among clinically relevant mycobacterial strains.

During severe infections, Staphylococcus aureus moves from its colonising sites to blood and tissues and is exposed to new selective pressures, thus, potentially driving adaptive evolution. Previous studies have shown the key role of the agr locus in S. aureus pathoadaptation; however, a more comprehensive characterisation of genetic signatures of bacterial adaptation may enable prediction of clinical outcomes and reveal new targets for treatment and prevention of these infections. Here, we measured adaptation using within-host evolution analysis of 2590 S . aureus genomes from 396 independent episodes of infection. By capturing a comprehensive repertoire of single nucleotide and structural genome variations, we found evidence of a distinctive evolutionary pattern within the infecting populations compared to colonising bacteria. These invasive strains had up to 20-fold enrichments for genome degradation signatures and displayed significantly convergent mutations in a distinctive set of genes, linked to antibiotic response and pathogenesis. In addition to agr -mediated adaptation, we identified non-canonical, genome-wide significant loci including sucA-sucB and stp1 . The prevalence of adaptive changes increased with infection extent, emphasising the clinical significance of these signatures. These findings provide a high-resolution picture of the molecular changes when S. aureus transitions from colonisation to severe infection and may inform correlation of infection outcomes with adaptation signatures. The bacterium Staphylococcus aureus lives harmlessly on our skin and noses. However, occasionally, it gets into our blood and internal organs, such as our bones and joints, where it causes severe, long-lasting infections that are difficult to treat. Over time, S. aureus acquire characteristics that help them to adapt to different locations, such as transitioning from the nose to the blood, and avoid being killed by antibiotics. Previous studies have identified changes, or ‘mutations’, in genes that are likely to play an important role in this evolutionary process. One of these genes, called accessory gene regulator (or agr for short), has been shown to control the mechanisms S. aureus use to infect cells and disseminate in the body. However, it is unclear if there are changes in other genes that also help S. aureus adapt to life inside the human body. To help resolve this mystery, Giulieri et al. collected 2,500 samples of S. aureus from almost 400 people. This included bacteria harmlessly living on the skin or in the nose, as well as strains that caused an infection. Gene sequencing revealed a small number of genes, referred to as ‘adaptive genes’, that often acquire mutations during infection. Of these, agr was the most commonly altered. However, mutations in less well-known genes were also identified: some of these genes are related to resistance to antibiotics, while others are involved in chemical processes that help the bacteria to process nutrients. Most mutations were caused by random errors being introduced in to the bacteria’s genetic code which stopped genes from working. However, in some cases, genes were turned off by small fragments of DNA moving around and inserting themselves into different parts of the genome. This study highlights a group of genes that help S. aureus to thrive inside the body and cause severe and prolonged infections. If these results can be confirmed, it may help to guide which antibiotics are used to treat different infections. Furthermore, understanding which genes are important for infection could lead to new strategies for eliminating this dangerous bacterium.

Mucosal‐associated invariant T (MAIT) cells represent up to 10% of circulating human T cells. They are usually defined using combinations of non‐lineage‐specific (surrogate) markers such as anti‐TRAV1‐2, CD161, IL‐18Rα and CD26. The development of MR1‐Ag tetramers now permits the specific identification of MAIT cells based on T‐cell receptor specificity. Here, we compare these approaches for identifying MAIT cells and show that surrogate markers are not always accurate in identifying these cells, particularly the CD4+ fraction. Moreover, while all MAIT cell subsets produced comparable levels of IFNγ, TNF and IL‐17A, the CD4+ population produced more IL‐2 than the other subsets. In a human ontogeny study, we show that the frequencies of most MR1 tetramer+ MAIT cells, with the exception of CD4+ MAIT cells, increased from birth to about 25 years of age and declined thereafter. We also demonstrate a positive association between the frequency of MAIT cells and other unconventional T cells including Natural Killer T (NKT) cells and Vδ2+ γδ T cells. Accordingly, this study demonstrates that MAIT cells are phenotypically and functionally diverse, that surrogate markers may not reliably identify all of these cells, and that their numbers are regulated in an age‐dependent manner and correlate with NKT and Vδ2+ γδ T cells. This study uses MR1 tetramers to enumerate and phenotypically characterize human blood MAIT cells, and subsets thereof based on CD4 and CD8 expression. Furthermore MR1 tetramers are compared to the commonly used mAb‐based MAIT cell identification techniques.

Whole genome sequencing (WGS) has been used to investigate transmission of Neisseria gonorrhoeae , but to date, most studies have not combined genomic data with detailed information on sexual behaviour to define the extent of transmission across population risk groups (bridging). Here, through combined epidemiological and genomic analysis of 2,186 N. gonorrhoeae isolates from Australia, we show widespread transmission of N. gonorrhoeae within and between population groups. We describe distinct transmission clusters associated with men who have sex with men (MSM) and heterosexuals, and men who have sex with men and women (MSMW) are identified as a possible bridging population between these groups. Further, the study identifies transmission of N. gonorrhoeae between HIV-positive and HIV-negative individuals receiving pre-exposure prophylaxis (PrEP). Our data highlight several groups that can be targeted for interventions aimed at improving gonorrhoea control, including returning travellers, sex workers, and PrEP users. Here, Williamson et al . combine epidemiological and genomic analysis of 2,186 Neisseria gonorrhoeae isolates from Australia and show that men who have sex with men and women are a possible ‘bridging’ population between men who have sex with men and heterosexuals.