Pathogen Genomes Research Articles

From cactus to crop: genomic insights of a beneficial and non-pathogenic Curtobacterium flaccumfaciens strain and the evolution of its pathosystem.

With the advent of advanced sequencing technologies, new insights into the genomes of pathogens, including those in the genus Curtobacterium, have emerged. This research investigates a newly isolated C. flaccumfaciens strain 208 (Cf208) from Arthrocereus glaziovii, and endemic plant from Iron Quadrangle. Previous results show that Cf208 exhibits the potential to remediate soils, facilitating the growth of tomato plants. Furthermore, Cf208 showed no virulence towards bean plants, thus, confounding its phytopathogenic origins. Using a comprehensive comparative genomics approach, we analyzed the Cf208 genome against 34 other Curtobacterium strains, aiming to discern the genomic landmarks associated with its adaptation as an endophyte and its avirulence in bean crops. This revealed a predominant core genome comprising about 2426 genes (68%). Notably, Cf208 possesses a unique plasmid, pCF208-73, which contains 84 unique genes (2.5%). However, unlike the plasmids previously described for pathogenic strains, pCF208-73 does not feature genes associated with virulence induction. In contrast, while several genes traditionally linked to virulence, like pectate lyases and proteases were identified, but the T4P apparatus emerged as new crucial factor for understanding virulence in the Curtobacterium genus. The presence or absence of this apparatus, especially in strains from different clades, may determine their virulence towards leguminous plants. In conclusion, this work highlights the significance of comparative genomics in unraveling the complexities of pathogenicity within the Curtobacterium genus. Our findings suggest that, although the limited genetic variations, specific genes, particularly those linked to the T4P apparatus, play a fundamental role in their interactions with host plants.

Genomic Epidemiology of Antimicrobial Resistance: Tracking the Global Spread of Resistant Pathogens

The rapid development and spread of antimicrobial resistance have been one of the most prominent health crises globally, which significantly challenges treating infectious diseases. This paper will address the contribution that genomic epidemiology makes to track and understand the global spread of antimicrobial-resistant pathogens. Genomic epidemiology, an area combining pathogen genomics with epidemiological data, allows for an extremely powerful approach in the identification of resistance mechanisms and tracing transmission routes for guiding public health interventions. This review Emphasizes major bacterial pathogens, such as Escherichia coli, Staphylococcus aureus, and Klebsiella pneumoniae, which harbor major resistance genes: blaCTX-M, mecA, and blaKPC. These genes contribute to the worldwide dissemination of resistance, mainly through mechanisms involving horizontal gene transfer and selective antibiotic pressure. Thus, genomic surveillance, phylogenetic analysis, and bioinformatics approaches allow for the pinpointing of transmission hotspots, mapping of resistance gene distribution, and tracking of emerging resistant strains in real time. It also provides information on how genomic data are being used to improve antimicrobial stewardship programs and inform clinical treatment decisions. Public health responses can further be tailored by recognizing regional resistance patterns, hence enhancing interventions. However, several challenges persist—for example, the need for standardized sharing of data, interpretation of genomic data, and integration of these insights into public health systems, especially in low-resource settings. This abstract highlights the transformative impact of genomic epidemiology in the management of the AMR crisis while emphasizing the continued need for global cooperation, enhanced data infrastructures, and policy initiatives to ensure the effective use of genomic data in public health.

Decoding Multidrug Resistance: Genetic Architecture and Codon Usage Patterns in ESKAPE Pathogens

Introduction: The escalating challenge of multidrug resistance among ESKAPE pathogens has become a prominent concern for global healthcare providers, leading to amplified morbidity and mortality rates. Method: Therefore, we conducted this study to elucidate the genetic architecture of ESKAPE constituents with the intent of ameliorating pathogenicity and facilitating drug development efforts. A comprehensive array of computational tools and statistical methodologies were employed to scrutinize the genomes of ESKAPE pathogens, revealing that translational selection profoundly influences the codon usage bias within this pathogenic cohort. Notably, leucine emerged as the predominantly amino acid, except in the case of Acinetobacter baumannii, where arginine exhibited preeminence. There was a universal preference for at least one histidine codon across all ESKAPE pathogens. GpC emerged as the most prominently overrepresented dinucleotide at the codon pair junction in all ESKAPE pathogens. Result: Furthermore, a comparison of gyrB gene sequences and phylogenic tree construction showed a distinct evolutionary relationship between AT-rich and GC-rich ESKAPE pathogens. Additionally, identification, characterization, and phylogenetic analysis of multiple antibiotic resistance genes revealed distinct evolutionary relationships. Conclusion: It was discerned that despite substantial variability amongst antibioticresistance genes, leucine emerged as the predominant amino acid. conclusion: Ultimately, we can say that codon and amino acid usage bias in ESKAPE pathogens and their ARGs genes has contributed the evolution of antibiotic resistance nature if the pathogens. Therefore, our study underscores the importance of continued surveillance and research efforts to address the growing threat of antibiotic resistance in ESKAPE pathogens .

Identifying factors that influence the use of pathogen genomics in Australia and New Zealand: a protocol.

Pathogen genomics, where whole genome sequencing technologies are used to produce complete genomic sequences of pathogens, is being increasingly used for infectious disease surveillance and outbreak response. Although proof-of-concept studies have highlighted the viability of using pathogen genomics in public health, few studies have investigated how end-users utilize pathogen genomics in public health. We describe a protocol for a study that aims to identify key factors that influence the use of pathogen genomics to inform public health responses against infectious diseases in Australia and New Zealand. We will use qualitative comparative analysis (QCA), a case-oriented methodology that systematically compares and analyses multiple cases (or 'units of analysis'), to identify multiple pathways leading to the use of pathogen genomics results in public health actions. As part of the process, we will develop a rubric to identify and define the use of pathogen genomics and individual factors affecting this process. Simultaneously, we will identify cases where pathogen genomics has been used in public health across Australia and New Zealand. Data for these cases will be collected from document review of publicly available and confidential documents and semi-structured interviews with technicians and end-users and summarized in a case report. These case reports will form the basis for scoring each case on the extent of the use of pathogen genomics data and the presence or absence of specific factors such as the ease of extracting essential information from pathogen genomics reports and perceptions toward pathogen genomics. Using the scores, cases will be analyzed using QCA techniques to identify pathways leading to the use of pathogen genomics data. These pathways will be interpreted alongside the cases to provide rich explanations of the use of pathogen genomics in public health. This study will improve our understanding of the key factors that facilitate or hinder the use of pathogen genomics to inform public health authorities and end-users. These findings may inform ways to enhance the use of pathogen genomics data in public health.

EnteroBase in 2025: exploring the genomic epidemiology of bacterial pathogens.

This paper presents an update on the content, accessibility and analytical tools of the EnteroBase platform for web-based pathogen genome analysis. EnteroBase provides manually curated databases of genome sequence data and associated metadata from currently>1.1 million bacterial isolates, more recently including Streptococcus spp. and Mycobacterium tuberculosis, in addition to Salmonella,Escherichia/Shigella,Clostridioides,Vibrio,Helicobacter,YersiniaandMoraxella. We have implemented the genome-based detection of antimicrobial resistance determinants and the new bubble plot graphical tool for visualizing bacterial genomic population structures, based on pre-computed hierarchical clusters. Access to data and analysis tools is provided through an enhanced graphical user interface and a new application programming interface (RESTful API). EnteroBase is now being developed and operated by an international consortium, to accelerate the development of the platform and ensure the longevity of the resources built. EnteroBase can be accessed at https://enterobase.warwick.ac.uk as well as https://enterobase.dsmz.de.

AMRColab - a user-friendly antimicrobial resistance detection and visualization tool.

Antimicrobial resistance (AMR) poses a significant threat to global public health, with the potential to cause millions of deaths annually by 2050. Effective surveillance of AMR pathogens is crucial for monitoring and predicting their behaviour in response to antibiotics. However, many public health professionals lack the necessary bioinformatics skills and resources to analyse pathogen genomes effectively. To address this challenge, we developed AMRColab, an open-access bioinformatics analysis suite hosted on Google Colaboratory. AMRColab enables users with limited or no bioinformatics training to detect and visualize AMR determinants in pathogen genomes using a 'plug-and-play' approach. The platform integrates established bioinformatics tools such as AMRFinderPlus and hAMRonization, allowing users to analyse, compare and visualize trends in AMR pathogens easily. A trial run using methicillin-resistant Staphylococcus aureus (MRSA) strains demonstrated AMRColab's effectiveness in identifying AMR determinants and facilitating comparative analysis across strains. A workshop was conducted and feedback from participants indicated high confidence in using AMRColab and a willingness to incorporate it into their research. AMRColab's user-friendly interface and modular design make it accessible to a diverse audience, including medical laboratory technologists, medical doctors and public health scientists, regardless of their bioinformatics expertise. Future improvements to AMRColab will include enhanced visualization tools, multilingual support and the establishment of an online community platform. AMRColab represents a significant step towards democratizing AMR surveillance and empowering public health professionals to combat AMR effectively.

A-238 Lyophilized Bead Multiplex Assays for CT/NG/TV Detection

Abstract Background Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), and Trichomonas vaginalis (TV) cause the three most prevalent sexually transmitted infectious diseases in developed countries, contributing to approximately 300 million new infections worldwide, annually. Due to the challenges of culturing and using immunoassays to detect these pathogens, DNA-based molecular tests have been instrumental, and current trends in molecular testing for CT/NG/TV have moved toward point-of-care tests. Lyophilization provides benefits required for point-of-care tests, such as ambient storage, extended shelf life, simplified downstream workflow, and variable formats. A lyophilized-bead format enables an assay to be placed in sealed consumables and stored at room temperature for up to two years. An additional consideration is the need for endogenous and exogenous controls. Exogenous in-process controls can be used to validate the performance of workflows culminating in a qPCR reaction and provide flexibility to test developers. An ideal exogenous in-process control is nonhomologous to human or human pathogen genome sequences and does not align with primers or probes designed for pathogen detection. The objective of the current study was to develop a lyophilized-bead-format assay containing an ideal exogenous control and establish whether it is a valid option for the accurate detection of CT, NG, and TV. Methods A synthetic DNA control containing targets for CT/NG/TV/RNase P (RP)/exogenous control was used to establish an analytical LOD by qPCR. The 5-plex assay, lyophilized in beads, was hydrated and tested for linearity over an 8-log range of the synthetic DNA control. Further, the qPCR lyophilized bead with assay was tested with a proficiency sample by extracting DNA from ZeptoMetrix NATtrol CT/NG/TV Positive Control and spiking-in the exogenous DNA control. qPCR was performed on the extracted DNA and human cDNA. Results The analytical LOD for the control DNA was determined to be 5 copies/reaction. Additionally, linearity was observed by qPCR over an 8-log range of the synthetic DNA control for all five targets in multiplex with good efficiencies and R2 values. Finally, all targets were amplified and detected by qPCR using DNA extracted from ZeptoMetrix NATtrol CT/NG/TV Positive Control with spiked-in exogenous DNA control. The detection of the exogenous sequence in the qPCR multiplex reaction indicated the extraction was successful. Conclusions We have developed a multiplex qPCR assay in lyophilized-bead format that can identify infectious agents, such as CT/NG/TV, in a single reaction. We have included RP as a human host control, plus an exogenous in-process control, for a total of 5-plex in separate fluorescent channels. We have shown that our lyophilized beads with assay produce a positive qPCR result when combined with a proficiency sample.

Impact of pneumococcal conjugate vaccines on invasive pneumococcal disease-causing lineages among South African children

Invasive pneumococcal disease (IPD) due to non-vaccine serotypes after the introduction of pneumococcal conjugate vaccines (PCV) remains a global concern. This study used pathogen genomics to evaluate changes in invasive pneumococcal lineages before, during and after vaccine introduction in South Africa. We included genomes (N = 3104) of IPD isolates from individuals aged <18 years (2005–20), spanning four periods: pre-PCV, PCV7, early-PCV13, and late-PCV13. Significant incidence reductions occurred among vaccine-type lineages in the late-PCV13 period compared to the pre-PCV period. However, some vaccine-type lineages continued to cause invasive disease and showed increasing effective population size trends in the post-PCV era. A significant increase in lineage diversity was observed from the PCV7 period to the early-PCV13 period (Simpson’s diversity index: 0.954, 95% confidence interval 0.948-0.961 vs 0.965, 0.962-0.969) supporting intervention-driven population structure perturbation. Increases in the prevalence of penicillin, erythromycin, and multidrug resistance were observed among non-vaccine serotypes in the late-PCV13 period compared to the pre-PCV period. In this work we highlight the importance of continued genomic surveillance to monitor disease-causing lineages post vaccination to support policy-making and future vaccine designs and considerations.

Toxin-mediated depletion of NAD and NADP drives persister formation in a human pathogen

Toxin–antitoxin (TA) systems are widespread in bacteria and implicated in genome stability, virulence, phage defense, and persistence. TA systems have diverse activities and cellular targets, but their physiological roles and regulatory mechanisms are often unclear. Here, we show that the NatR–NatT TA system, which is part of the core genome of the human pathogen Pseudomonas aeruginosa, generates drug-tolerant persisters by specifically depleting nicotinamide dinucleotides. While actively growing P. aeruginosa cells compensate for NatT-mediated NAD+ deficiency by inducing the NAD+ salvage pathway, NAD depletion generates drug-tolerant persisters under nutrient-limited conditions. Our structural and biochemical analyses propose a model for NatT toxin activation and autoregulation and indicate that NatT activity is subject to powerful metabolic feedback control by the NAD+ precursor nicotinamide. Based on the identification of natT gain-of-function alleles in patient isolates and on the observation that NatT increases P. aeruginosa virulence, we postulate that NatT modulates pathogen fitness during infections. These findings pave the way for detailed investigations into how a toxin–antitoxin system can promote pathogen persistence by disrupting essential metabolic pathways.

Pathogen genomic surveillance status among lower resource settings in Asia

Asia remains vulnerable to new and emerging infectious diseases. Understanding how to improve next generation sequencing (NGS) use in pathogen surveillance is an urgent priority for regional health security. Here we developed a pathogen genomic surveillance assessment framework to assess capacity in low-resource settings in South and Southeast Asia. Data collected between June 2022 and March 2023 from 42 institutions in 13 countries showed pathogen genomics capacity exists, but use is limited and under-resourced. All countries had NGS capacity and seven countries had strategic plans integrating pathogen genomics into wider surveillance efforts. Several pathogens were prioritized for human surveillance, but NGS application to environmental and human–animal interface surveillance was limited. Barriers to NGS implementation include reliance on external funding, supply chain challenges, trained personnel shortages and limited quality assurance mechanisms. Coordinated efforts are required to support national planning, address capacity gaps, enhance quality assurance and facilitate data sharing for decision making.

Unambiguous Detection of LTR-III G-Quadruplex in the HIV Genome Using a Tailored Fluorogenic Probe-based Assay.

The noncanonical conformations within the genomes of viral pathogens is of significant diagnostic value, due to their unique secondary structures and interactions with specific fluorogenic molecules. In particular, adaptation of the G-quadruplex (GQ) conformation by the specific gene sequence leads to distinct topological features, resulting in unique binding sites that are crucial for the selective recognition of human immunodeficiency virus (HIV) by small molecules. Leveraging the selective fluorescence response of a benzobisthiazole-based fluorogenic probe to the LTR-III GQ target, we developed a GQ-based diagnostic platform for HIV detection. The successful fluorescence recognition of an amplified 176-nucleotide genomic segment harboring the LTR-III GQ, facilitated by pH-controlled GQ-targeted reliable conformational polymorphism (GQ-RCP), validates this method as an effective GQ-topology-targeted diagnostic tool for HIV.

Prediction of antimicrobial resistance of Klebsiella pneumoniae from genomic data through machine learning.

Antimicrobials, such as antibiotics or antivirals are medications employed to prevent and treat infectious diseases in humans, animals, and plants. Antimicrobial Resistance occurs when bacteria, viruses, and parasites no longer respond to these medicines. This resistance renders antibiotics and other antimicrobial drugs ineffective, making infections challenging or impossible to treat. This escalation in drug resistance heightens the risk of disease spread, severe illness, disability, and mortality. With datasets now containing hundreds or even thousands of pathogen genomes, machine learning techniques are on the rise for predicting antibiotic resistance in pathogens, prediction based on gene content and genome composition. Aim of this work is to combine and incorporate machine learning methods on bacterial genomic data to predict antimicrobial resistance, we will focus on the case of Klebsiella pneumoniae in order to support clinicians in selecting appropriate therapy.

Optimizing next-generation sequencing efficiency in clinical settings: analysis of read length impact on cost and performance

BackgroundThe expansion of sequencing technologies as a result of the response to the COVID-19 pandemic enabled pathogen (meta)genomics to be deployed as a routine component of surveillance in many countries. Scaling genomic surveillance, however, comes with associated costs in both equipment and sequencing reagents, which should be optimized. Here, we evaluate the cost efficiency and performance of different read lengths in identifying pathogens in metagenomic samples. We carefully evaluated performance metrics, costs, and time requirements relative to choices of 75, 150 and 300 base pairs (bp) read lengths in pathogen identification.ResultsOur findings revealed that moving from 75 bp to 150 bp read length approximately doubles both the cost and sequencing time. Opting for 300 bp reads leads to approximately two- and three-fold increases, respectively, in cost and sequencing time compared to 75 bp reads. For viral pathogen detection, the sensitivity median ranged from 99% with 75 bp reads to 100% with 150–300 bp reads. However, bacterial pathogens detection was less effective with shorter reads: 87% with 75 bp, 95% with 150 bp, and 97% with 300 bp reads. These findings were consistent across different levels of taxa abundance. The precision of pathogen detection using shorter reads was comparable to that of longer reads across most viral and bacterial taxa.ConclusionsDuring disease outbreak situations, when swift responses are required for pathogen identification, we suggest prioritizing 75 bp read lengths, especially if detection of viral pathogens is aimed. This practical approach allows better use of resources, enabling the sequencing of more samples using streamlined workflows, while maintaining a reliable response capability.

The rise of pathogen genomics in Africa.

The routine genomic surveillance of pathogens in diverse geographical settings and equitable data sharing are critical to inform effective infection control and therapeutic development. The coronavirus disease 2019 (COVID-19) pandemic highlighted the importance of routine genomic surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to detect emerging variants of concern. However, the majority of high-income countries sequenced >0.5% of their COVID-19 cases, unlike low- and middle-income countries. By the end of 2022, many countries around the world had managed to establish capacity for pathogen genomic surveillance. Notably, Beta and Omicron; 2 of the 5 current SARS-CoV-2 variants of concern were first discovered in Africa through an aggressive sequencing campaign led by African scientists. To sustain such infrastructure and expertise beyond this pandemic, other endemic pathogens should leverage this investment. Therefore, countries are establishing multi-pathogen genomic surveillance strategies. Here we provide a catalog of the current landscape of sequenced and publicly shared pathogens in different countries in Africa. Drawing upon our collective knowledge and expertise, we review the ever-evolving challenges and propose innovative recommendations.

Turning the needle into the haystack: Culture-independent amplification of complex microbial genomes directly from their native environment.

High-throughput sequencing (HTS) has revolutionized microbiology, but many microbes exist at low abundance in their natural environment and/or are difficult, if not impossible, to culture in the laboratory. This makes it challenging to use HTS to study the genomes of many important microbes and pathogens. In this review, we discuss the development and application of selective whole genome amplification (SWGA) to allow whole or partial genomes to be sequenced for low abundance microbes directly from complex biological samples. We highlight ways in which genomic data generated by SWGA have been used to elucidate the population dynamics of important human pathogens and monitor development of antimicrobial resistance and the emergence of potential outbreaks. We also describe the limitations of this method and propose some potential innovations that could be used to improve the quality of SWGA and lower the barriers to using this method across a wider range of infectious pathogens.

The Role of Genomics in Infectious Disease Control

The integration of genomics into infectious disease control has revolutionized pathogen detection, surveillance, and treatment strategies. With advancements in next-generation sequencing (NGS), genomic technologies have enabled the identification of infectious agents, tracing their transmission, and monitoring of antimicrobial resistance. Genomic epidemiology offers a powerful tool for outbreak investigations by identifying transmission chains and understanding pathogen evolution. Furthermore, genomics supports precision medicine, allowing for tailored treatments based on both pathogen and host genomes. This paper reviews the applications of genomics in disease detection, outbreak management, antimicrobial resistance surveillance, and precision medicine, underscoring its pivotal role in global public health efforts. The future of infectious disease control will be shaped by genomics, with an emphasis on enhanced diagnostics, personalized treatment, and real-time pathogen tracking. Keywords: Genomics, Infectious Diseases, Next-Generation Sequencing, Outbreak Investigation, Genomic Epidemiology.

The utility of whole-genome sequencing to identify likely transmission pairs for pathogens with slow and variable evolution

Pathogen whole-genome sequencing (WGS) has been used to track the transmission of infectious diseases in extraordinary detail, especially for pathogens that undergo fast and steady evolution, as is the case with many RNA viruses. However, for other pathogens evolution is less predictable, making interpretation of these data to inform our understanding of their epidemiology more challenging and the value of densely collected pathogen genome data uncertain. Here, we assess the utility of WGS for one such pathogen, in the “who-infected-whom” identification problem. We study samples from hosts (130 cattle, 111 badgers) with confirmed infection of M. bovis (causing bovine Tuberculosis), which has an estimated clock rate as slow as ∼0.1–1 variations per year. For each potential pathway between hosts, we calculate the relative likelihood that such a transmission event occurred. This is informed by an epidemiological model of transmission, and host life history data. By including WGS data, we shrink the number of plausible pathways significantly, relative to those deemed likely on the basis of life history data alone. Despite our uncertainty relating to the evolution of M. bovis, the WGS data are therefore a valuable adjunct to epidemiological investigations, especially for wildlife species whose life history data are sparse.

Inference of host-pathogen interaction matrices from genome-wide polymorphism data.

Host-pathogen coevolution is defined as the reciprocal evolutionary changes in both species due to genotype x genotype (GxG) interactions at the genetic level determining the outcome and severity of infection. While co-analyses of host and pathogen genomes (co-GWAs) allow us to pinpoint the interacting genes, these do not reveal which host genotype(s) is/are resistant to which pathogen genotype(s). The knowledge of this so-called infection matrix is important for agriculture and medicine. Building on established theories of host-pathogen interactions, we here derive four novel indices capturing the characteristics of the infection matrix. These indices can be computed from full genome polymorphism data of randomly sampled uninfected hosts, as well as infected hosts and their pathogen strains. We use these indices in an Approximate Bayesian Computation method to pinpoint loci with relevant GxG interactions and to infer their underlying interaction matrix. In a combined SNP data set of 451 European humans and their infecting Hepatitis C Virus (HCV) strains and 503 uninfected individuals, we reveal a new human candidate gene for resistance to HCV and new virus mutations matching human genes. For two groups of significant human-HCV (GxG) associations, we infer a gene-for-gene infection matrix, which is commonly assumed to be typical of plant-pathogen interactions. Our model-based inference framework bridges theoretical models of GxG interactions with host and pathogen genomic data. It, therefore, paves the way for understanding the evolution of key GxG interactions underpinning HCV adaptation to the European human population after a recent expansion.

Data sharing considerations to maximize the use of pathogen biological and genomics resources data for public health.

Public sector data associated with health are a highly valuable resource with multiple potential end-users, from health practitioners, researchers, public bodies, policy makers, and industry. Data for infectious disease agents are used for epidemiological investigations, disease tracking and assessing emerging biological threats. Yet, there are challenges in collating and re-using it. Data may be derived from multiple sources, generated and collected for different purposes. While public sector data should be open access, providers from public health settings or from agriculture, food, or environment sources have sensitivity criteria to meet with ethical restrictions in how the data can be reused. Yet, sharable datasets need to describe the pathogens with sufficient contextual metadata for maximal utility, e.g. associated disease or disease potential and the pathogen source. As data comprise the physical resources of pathogen collections and potentially associated sequences, there is an added emerging technical issue of integration of omics 'big data'. Thus, there is a need to identify suitable means to integrate and safely access diverse data for pathogens. Established genomics alliances and platforms interpret and meet the challenges in different ways depending on their own context. Nonetheless, their templates and frameworks provide a solution for adaption to pathogen datasets.

The re-emergence of sexually transmissible multidrug resistant Shigella flexneri 3a, England, United Kingdom

Shigellosis is an enteric infection that transmits through the faecal-oral route, which can occur during sex between men who have sex with men (MSM). Between 2009 and 2014, an epidemic of sexually transmissible Shigella flexneri 3a occurred in England that subsequently declined. However, from 2019 to 2021, despite SARS-CoV-2 restrictions, S. flexneri 3a continued to re-emerge. We explored possible drivers of re-emergence by comparing host demography and pathogen genomics. Cases were primarily among 35–64 year old men in London. Genomic analyses of 502 bacterial isolates showed that the majority (58%) of re-emerging MSM strains were a clonal replacement of the original, with reduced antimicrobial resistance, conservation of plasmid col156_1, and two SNPs with 19 predicted effects. The absence of major changes in the pathogen or host demographics suggest that other factors may have driven the re-emergence of S. flexneri 3a and highlight the need for further work in the area.