Defining genomic epidemiology thresholds for common-source bacterial outbreaks: a modelling study

Epidemiology of Staphylococcus aureus food isolates: Comparison of conventional methods with whole genome sequencing typing methods

A variety of methods exists for typing bacteria. However, guidelines for the application and interpretation of typing tools in epidemiologic investigations of Staphylococcus aureus are lacking. This study aimed to identify appropriate typing methods for S. aureus population studies and outbreak investigation. We compared pulsed-field gel electrophoresis (PFGE), seven loci multi-locus sequence typing (MLST), core genome MLST (cgMLST), core single nucleotide polymorphism (cSNP), and enterotoxin (se/SE) profiles on 351 S. aureus isolates. The discriminatory power, concordance, and congruence of typing results were assessed. cgMLST, cSNP, and PFGE yielded the highest discrimination value, followed by se/SE typing and MLST. The best concordance of results was found between cgMLST and cSNP, while the best congruence was observed for cgMLST and cSNP with all methods, followed by PFGE with MLST. The strengths and weaknesses of each method are highlighted. For population structure, cgMLST and cSNP performed better than PFGE and MLST in terms of resolution of clusters and in phylogenetic inference. Enterotoxin profiles matched with MLST groups, suggesting the use of se/SE typing to predict MLST results. For the retrospective analysis of 31 outbreaks, all methods performed almost equally to discriminate epidemiologically related strains and can be used to unambiguously distinguish outbreak strains.

Genomic evidence of Escherichia coli gut population diversity translocation in leukemia patients

Escherichia coli, a commensal species of the human gut, is an opportunistic pathogen that can reach extra-intestinal compartments, including the bloodstream and the bladder, among others. In non-immunosuppressed patients, purifying or neutral evolution of E. coli populations has been reported in the gut. Conversely, it has been suggested that when migrating to extra-intestinal compartments, E. coli genomes undergo diversifying selection as supported by strong evidence for adaptation. The level of genomic polymorphism and the size of the populations translocating from gut to extra-intestinal compartments is largely unknown. To gain insights into the pathophysiology of these translocations, we investigated the level of polymorphism and the evolutionary forces acting on the genomes of 77 E. coli isolated from various compartments in three immunosuppressed patients. Each patient had a unique strain, which was a mutator in one case. In all instances, we observed that translocation encompasses much of the genomic diversity present in the gut. The same signature of selection, whether purifying or diversifying, and as anticipated, neutral for mutator isolates, was observed in both the gut and bloodstream. Additionally, we found a limited number of non-specific mutations among compartments for non-mutator isolates. In all cases, urine isolates were dominated by neutral selection. These findings indicate that substantial proportions of populations are undergoing translocation and that they present a complex compartment-specific pattern of selection at the patient level.IMPORTANCEIt has been suggested that intra and extra-intestinal compartments differentially constrain the evolution of E. coli strains. Whether host particular conditions, such as immunosuppression, could affect the strain evolutionary trajectories remains understudied. We found that, in immunosuppressed patients, large fractions of E. coli gut populations are translocating with variable modifications of the signature of selection for commensal and pathogenic isolates according to the compartment and/or the patient. Such multiple site sampling should be performed in large cohorts of patients to gain a better understanding of E. coli extra-intestinal diseases.

WGS Analysis of Staphylococcus warneri Outbreak in a Neonatal Intensive Care Unit

Purpose

Staphylococcus warneri is an opportunistic pathogen responsible for hospital-acquired infections (HAIs). The aim of this study was to describe an outbreak caused by S. warneri infection in a neonatal intensive care unit (NICU) and provide investigation, prevention and control strategies for this outbreak.

Methods

We conducted an epidemiological investigation of the NICU S. warneri outbreak, involving seven neonates, staff, and environmental screening, to identify the source of infection. WGS analyses were performed on S. warneri isolates, including species identification, core genome single-nucleotide polymorphism (cgSNP) analysis, pan-genome analysis, and genetic characterization assessment of the prevalence of specific antibiotic resistance and virulence genes.

Results

Eight S. warneri strains were isolated from this outbreak, with seven from neonates and one from environment. Six clinical cases within three days in 2021 were linked to one strain isolated from environmental samples; isolates varied by 0-69 SNPs and were confirmed to be from an outbreak through WGS. Multiple infection prevention measures were implemented, including comprehensive environmental disinfection and stringent protocols, and all affected neonates were transferred to the isolation wards. Following these interventions, no further cases of S. warneri infections were observed. Furthermore, pan-genome analysis results suggested that in human S. warneri may exhibit host specificity.

Conclusion

The investigation has revealed that the outbreak was linked to the milk preparation workbench by the WGS. It is recommended that there be a stronger focus on environmental disinfection management in order to raise awareness, improve identification, and prevention of healthcare-associated infections that are associated with the hospital environment.

Use of whole genome sequencing for surveillance and control of foodborne diseases: status quo and quo vadis

Improvements in sequencing quality, availability, speed and costs results in an increased presence of genomics in infectious disease applications. Nevertheless, there are still hurdles in regard to the optimal use of WGS for public health purposes. Here, we discuss the current state ("status quo") and future directions ("quo vadis") based on literature regarding the use of genomics in surveillance, hazard characterization and source attribution of foodborne pathogens. The future directions include the application of new techniques, such as machine learning and network approaches that may overcome the current shortcomings. These include the use of fixed genomic distances in cluster delineation, disentangling similarity or lack thereof in source attribution, and difficulties ascertaining function in hazard characterization. Although, the aforementioned methods can relatively easily be applied technically, an overarching challenge is the inference and biological/epidemiological interpretation of these large amounts of high-resolution data. Understanding the context in terms of bacterial isolate and host diversity allows to assess the level of representativeness in regard to sources and isolates in the dataset, which in turn defines the level of certainty associated with defining clusters, sources and risks. This also marks the importance of metadata (clinical, epidemiological, and biological) when using genomics for public health purposes.

Foodborne bacterial pathogens: genome-based approaches for enduring and emerging threats in a complex and changing world

Foodborne illnesses pose a substantial health and economic burden, presenting challenges in prevention due to the diverse microbial hazards that can enter and spread within food systems. Various factors, including natural, political and commercial drivers, influence food production and distribution. The risks of foodborne illness will continue to evolve in step with these drivers and with changes to food systems. For example, climate impacts on water availability for agriculture, changes in food sustainability targets and evolving customer preferences can all have an impact on the ecology of foodborne pathogens and the agrifood niches that can carry microorganisms. Whole-genome and metagenome sequencing, combined with microbial surveillance schemes and insights from the food system, can provide authorities and businesses with transformative information to address risks and implement new food safety interventions across the food chain. In this Review, we describe how genome-based approaches have advanced our understanding of the evolution and spread of enduring bacterial foodborne hazards as well as their role in identifying emerging foodborne hazards. Furthermore, foodborne hazards exist in complex microbial communities across the entire food chain, and consideration of these co-existing organisms is essential to understanding the entire ecology supporting pathogen persistence and transmission in an evolving food system.

Persistence of commensal multidrug-resistant Escherichia coli in the broiler production pyramid is best explained by strain recirculation from the rearing environment

Despite the success of mitigation policies in several countries to reduce the use of antibiotics in veterinary medicine, pathogenic and commensal bacteria resistant to antibiotics are still circulating in livestock animals. However, factors contributing the most to antimicrobial resistance (AMR) persistence in these settings are yet not clearly identified. The broiler production, with its highly segmented, pyramidal structure offers an ideal context to understand and control the spread of resistant bacteria. By taking advantage of an experimental facility reproducing the whole broiler production pyramid, we demonstrate that resistant E. coli persist in our system primarily though recirculation of a few commensal clones surviving in the rearing environment. No vertical transmission from hens to offspring nor strain acquisition at the hatchery were detected, while import of new strains from outside the facility seems limited. Moreover, each clone carries its own resistance-conferring plasmid(s), and a single putative plasmid horizontal transfer could have been inferred. These results, observed for now in a small experimental facility with high level of biosecurity, must be confirmed in a commercial farm context but still provide invaluable information for future mitigation policies.

DODGE: automated point source bacterial outbreak detection using cumulative long term genomic surveillance

SUMMARY

The reliable and timely recognition of outbreaks is a key component of public health surveillance for foodborne diseases. Whole genome sequencing (WGS) offers high resolution typing of foodborne bacterial pathogens and facilitates the accurate detection of outbreaks. This detection relies on grouping WGS data into clusters at an appropriate genetic threshold. However, methods and tools for selecting and adjusting such thresholds according to the required resolution of surveillance and epidemiological context are lacking. Here we present DODGE (Dynamic Outbreak Detection for Genomic Epidemiology), an algorithm to dynamically select and compare these genetic thresholds. DODGE can analyse expanding datasets over time and clusters that are predicted to correspond to outbreaks (or "investigation clusters") can be named with established genomic nomenclature systems to facilitate integrated analysis across jurisdictions. DODGE was tested in two real-world Salmonella genomic surveillance datasets of different duration, 2 months from Australia and 9 years from the United Kingdom. In both cases only a minority of isolates were identified as investigation clusters. Two known outbreaks in the United Kingdom dataset were detected by DODGE and were recognized at an earlier timepoint than the outbreaks were reported. These findings demonstrated the potential of the DODGE approach to improve the effectiveness and timeliness of genomic surveillance for foodborne diseases and the effectiveness of the algorithm developed.

AVAILABILITY AND IMPLEMENTATION

DODGE is freely available at https://github.com/LanLab/dodge and can easily be installed using Conda.

Genomic insights into the expansion of carbapenem-resistant Klebsiella pneumoniae within Portuguese hospitals

Carbapenem-resistant Klebsiella pneumoniae (CR-KP) are a public health concern, causing infections with a high mortality rate, limited therapeutic options and challenging infection control strategies. In Portugal, the CR-KP rate has increased sharply, but the factors associated with this increase are poorly explored. In order to address this question, phylogenetic and resistome analysis were used to compare the draft genomes of 200 CR-KP isolates collected in 2017-2019 from five hospitals in the Lisbon region, Portugal. Most CR-KP belonged to sequence type (ST) 13 (29%), ST17 (15%), ST348 (13%), ST231 (12%) and ST147 (7%). Carbapenem resistance was conferred mostly by the presence of KPC-3 (74%) or OXA-181 (18%), which were associated with IncF/IncN and IncX plasmids, respectively. Almost all isolates were multi-drug resistant, harbouring resistance determinants to aminoglycosides, beta-lactams, trimethoprim, fosfomycin, quinolones and sulphonamides. In addition, 11% of isolates were resistant to colistin. Colonizing and infecting isolates were highly related, and most colonized patients (89%) reported a previous hospitalization. Moreover, among the 171 events of cross-dissemination identified by core genome multi-locus sequence typing data analysis (fewer than five allelic differences), 41 occurred between different hospitals and 130 occurred within the same hospital. The results suggest that CR-KP dissemination in the Lisbon region results from acquisition of carbapenemases in mobile genetic elements, influx of CR-KP into the hospitals by colonized ambulatory patients, and transmission of CR-KP within and between hospitals. Prudent use of carbapenems, patient screening at hospital entry, and improvement of infection control are needed to decrease the burden of CR-KP infection in Portugal.

Closing the gap: Oxford Nanopore Technologies R10 sequencing allows comparable results to Illumina sequencing for SNP-based outbreak investigation of bacterial pathogens

Whole-genome sequencing has become the method of choice for bacterial outbreak investigation, with most clinical and public health laboratories currently routinely using short-read Illumina sequencing. Recently, long-read Oxford Nanopore Technologies (ONT) sequencing has gained prominence and may offer advantages over short-read sequencing, particularly with the recent introduction of the R10 chemistry, which promises much lower error rates than the R9 chemistry. However, limited information is available on its performance for bacterial single-nucleotide polymorphism (SNP)-based outbreak investigation. We present an open-source workflow, Prokaryotic Awesome variant Calling Utility (PACU) (https://github.com/BioinformaticsPlatformWIV-ISP/PACU), for constructing SNP phylogenies using Illumina and/or ONT R9/R10 sequencing data. The workflow was evaluated using outbreak data sets of Shiga toxin-producing Escherichia coli and Listeria monocytogenes by comparing ONT R9 and R10 with Illumina data. The performance of each sequencing technology was evaluated not only separately but also by integrating samples sequenced by different technologies/chemistries into the same phylogenomic analysis. Additionally, the minimum sequencing time required to obtain accurate phylogenetic results using nanopore sequencing was evaluated. PACU allowed accurate identification of outbreak clusters for both species using all technologies/chemistries, but ONT R9 results deviated slightly more from the Illumina results. ONT R10 results showed trends very similar to Illumina, and we found that integrating data sets sequenced by either Illumina or ONT R10 for different isolates into the same analysis produced stable and highly accurate phylogenomic results. The resulting phylogenies for these two outbreaks stabilized after ~20 hours of sequencing for ONT R9 and ~8 hours for ONT R10. This study provides a proof of concept for using ONT R10, either in isolation or in combination with Illumina, for rapid and accurate bacterial SNP-based outbreak investigation.

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.

Cross-contamination of carbapenem-resistant Gram-negative bacteria between patients and the hospital environment in the first year of a newly built surgical ward

BACKGROUND

Transmission and outbreaks of carbapenem-resistant Gram-negative bacteria (CRGN) in hospitals are often associated with contamination of the wastewater environment. We performed a prospective observational study to investigate the colonization of the hospital wastewater environment during the first year of occupancy of the surgical intermediate and intensive care units of a newly constructed building at the University Hospital of Heidelberg, Germany.

METHODS

We performed monthly screening of the wastewater system (toilets and sinks) for 12 months, starting 1 month before opening (1st October 2020 to 30th October 2021). Admission and weekly rectal screening of patients for CRGN were also performed in parallel. Bacterial isolates were characterized by whole-genome sequencing.

RESULTS

Twenty-seven of 1978 (1.4%) admitted patients were colonized/infected with CRGN. A total of 29 CRGN isolates from 24 patients and 52 isolates were available for sequencing. Within the first month of occupancy, we identified seven patients colonized/infected with CRGN, while none were found in the environmental reservoirs. The first detection of CRGN isolates in the sewage system started five months after the first occupancy. Two previously non-colonized patients were colonized/infected with Pseudomonas aeruginosa strains colonizing the sewage system. The significant identity of plasmids carrying the carbapenemase gene suggests that long-term colonization of the sewage system facilitates the emergence of new carbapenem-resistant clones.

CONCLUSION

Cross-contamination between patients and the hospital environment is bidirectional. Our study demonstrated that contamination of the hospital wastewater environment may lead to persistent colonization and may serve as a reservoir for nosocomial acquisition of CRGN.

Investigating environmental transmission to resolve a Bacillus cereus group outbreak in a neonatal intensive care unit using core genome multilocus sequence typing

BACKGROUND

We analyzed an outbreak of Bacillus cereus group (Bcg) at a single-center neonatal intensive care unit level IV by conducting comprehensive sampling of both patients and the environment.

METHODS

Between 06/2020 and 10/2021, all Bcg isolates identified by both regular colonization screening and additional sampling of the environment were subjected to whole-genome sequencing, followed by in vitro extraction of MLST ST, resistance genes and virulence factors. Using publicly available genome sequences, we defined an ad hoc core genome multilocus sequence typing (cgMLST) scheme comprising 2759 target genes for Bcg typing, which we applied to the detected isolates. We have compared the results with a stable cgMLST that was published in the meantime and completed the investigation with a SNP analysis.

RESULTS

We analyzed 28 Bcg isolates from patient and environmental samples using MLST and cgMLST. This revealed multiple sequence types, with ST127 being the most common (n = 13). Both cgMLST schemes grouped ten of the 13 ST127 isolates into a cluster, including two invasive isolates from two different patients and several environmental samples. SNP analysis postulated a screen from a ventilation machine as a possible reservoir.

CONCLUSION

In sensitive settings such as neonatal intensive care units, considering the environment in outbreak analyses is crucial, especially when investigating potential transmission routes through shared devices. When dealing with widespread bacteria such as Bcg, high-resolution typing techniques are necessary. In this study, we successfully resolved an outbreak of Bcg infections using a custom cgMLST scheme combined with a SNP analysis.

Persistence of microbiological hazards in food and feed production and processing environments

Listeria monocytogenes (in the meat, fish and seafood, dairy and fruit and vegetable sectors), Salmonella enterica (in the feed, meat, egg and low moisture food sectors) and Cronobacter sakazakii (in the low moisture food sector) were identified as the bacterial food safety hazards most relevant to public health that are associated with persistence in the food and feed processing environment (FFPE). There is a wide range of subtypes of these hazards involved in persistence in the FFPE. While some specific subtypes are more commonly reported as persistent, it is currently not possible to identify universal markers (i.e. genetic determinants) for this trait. Common risk factors for persistence in the FFPE are inadequate zoning and hygiene barriers; lack of hygienic design of equipment and machines; and inadequate cleaning and disinfection. A well-designed environmental sampling and testing programme is the most effective strategy to identify contamination sources and detect potentially persistent hazards. The establishment of hygienic barriers and measures within the food safety management system, during implementation of hazard analysis and critical control points, is key to prevent and/or control bacterial persistence in the FFPE. Once persistence is suspected in a plant, a 'seek-and-destroy' approach is frequently recommended, including intensified monitoring, the introduction of control measures and the continuation of the intensified monitoring. Successful actions triggered by persistence of L. monocytogenes are described, as well as interventions with direct bactericidal activity. These interventions could be efficient if properly validated, correctly applied and verified under industrial conditions. Perspectives are provided for performing a risk assessment for relevant combinations of hazard and food sector to assess the relative public health risk that can be associated with persistence, based on bottom-up and top-down approaches. Knowledge gaps related to bacterial food safety hazards associated with persistence in the FFPE and priorities for future research are provided.

Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.

Monocentric observational cohort study to investigate the transmission of third-generation cephalosporin-resistant Enterobacterales in a neonatal intensive care unit in Heidelberg, Germany

Third-generation cephalosporin-resistant Enterobacterales is a major threat for newborns in neonatal intensive care units (NICUs). The route of acquisition in a non-outbreak setting should be investigated to implement adequate infection prevention measures. To identify risk factors for colonization with and to investigate the transmission pattern of third-generation cephalosporin-resistant Enterobacterales in a NICU setting. This monocentric observational cohort study in a tertiary NICU in Heidelberg, Germany, enrolled all hospitalized neonates screened for cephalosporin-resistant Enterobacterales. Data were collected from 1 January 2018 to 31 December 2021. Weekly screening by rectal swabs for colonization with third-generation cephalosporin-resistant Enterobacterales was performed for all newborns until discharge. Whole-genome sequencing was performed for molecular characterization and transmission analysis. In total, 1,287 newborns were enrolled. The median length of stay was 20 (range 1-250) days. Eighy-eight infants (6.8%) were colonized with third-generation cephalosporin-resistant Enterobacterales. Low birth weight [<1500 g (adjusted odds ratio, 5.1; 95% CI 2.2-11.5; P < 0.001)] and longer hospitalization [per 30 days (adjusted odds ratio, 1.7; 95% CI 1.5-2.0; P < 0.001)] were associated with colonization or infection with drug-resistant Enterobacterales in a multivariate analysis. Enterobacter cloacae complex was the most prevalent third-generation cephalosporin-resistant Enterobacterales detected, 64.8% (59 of 91). Whole-genome sequencing, performed for the available 85 of 91 isolates, indicated 12 transmission clusters involving 37 patients. This cohort study suggests that transmissions of third-generation cephalosporin-resistant Enterobacterales in newborns occur frequently in a non-outbreak NICU setting, highlighting the importance of surveillance and preventive measures in this vulnerable patient group. IMPORTANCE Preterm newborns are prone to infections. Therefore, infection prevention should be prioritized in this vulnerable patient group. However, outbreaks involving drug-resistant bacteria, such as third-generation resistant Enterobacterales, are often reported. Our study aims to investigate transmission and risk factors for acquiring third-generation cephalosporin-resistant Enterobacterales in a non-outbreak NICU setting. Our data indicated that premature birth and low birth weight are significant risk factors for colonization/infection with third-generation cephalosporin-resistant Enterobacterales. Furthermore, we could identify putative transmission clusters by whole-genome sequencing, highlighting the importance of preemptive measures to prevent infections in this patient collective.

P-DOR, an easy-to-use pipeline to reconstruct bacterial outbreaks using genomics

SUMMARY

Bacterial Healthcare-Associated Infections (HAIs) are a major threat worldwide, which can be counteracted by establishing effective infection control measures, guided by constant surveillance and timely epidemiological investigations. Genomics is crucial in modern epidemiology but lacks standard methods and user-friendly software, accessible to users without a strong bioinformatics proficiency. To overcome these issues we developed P-DOR, a novel tool for rapid bacterial outbreak characterization. P-DOR accepts genome assemblies as input, it automatically selects a background of publicly available genomes using k-mer distances and adds it to the analysis dataset before inferring a Single-Nucleotide Polymorphism (SNP)-based phylogeny. Epidemiological clusters are identified considering the phylogenetic tree topology and SNP distances. By analyzing the SNP-distance distribution, the user can gauge the correct threshold. Patient metadata can be inputted as well, to provide a spatio-temporal representation of the outbreak. The entire pipeline is fast and scalable and can be also run on low-end computers.

AVAILABILITY AND IMPLEMENTATION

P-DOR is implemented in Python3 and R and can be installed using conda environments. It is available from GitHub https://github.com/SteMIDIfactory/P-DOR under the GPL-3.0 license.