Delineating community outbreaks of Salmonella enterica serovar Typhimurium by use of whole-genome sequencing: insights into genomic variability within an outbreak

Assessment and genomic analysis of Salmonella and Campylobacter from different stages of an integrated no-antibiotics-ever (NAE) broiler complex: a longitudinal study

The objective of this study was to determine prevalence and perform genomic analysis of Salmonella spp. and Campylobacter spp. isolated from different stages of an integrated NAE broiler complex. Environmental samples were screened with 3M-Molecular Detection System (MDS) and MDS positive samples were further processed for confirmation of results and identification. Core genome-based phylogenies were built for both bacteria isolated from this study along with selected NCBI genomes. The odds ratios and 95% confidence limits were compared among stages and sample types (α < 0.05) using multivariable model. Based on MDS results, 4% and 18% of total samples were positive for Salmonella spp. and Campylobacter spp. respectively. The odds of Salmonella detection in hatchery samples were 2.58 times as likely as compared to its detection in production farms' samples (P = 0.151) while the odds of Campylobacter detection in production farms' samples were 32.19 times as likely as its detection in hatchery (P = 0.0015). Similarly, the odds of Campylobacter detection in boot swabs, soil, water, and miscellaneous samples were statistically significant (P < 0.05) as compared with fly paper as reference group. The serovars identified for Salmonella were Typhimurium, Barranquilla, Liverpool, Kentucky, Enteritidis, Luciana, and Rough_O:r:1,5. For Campylobacter, the species identified were Campylobacter jejuni and Campylobacter coli. Phylogeny results show close genetic relatedness among bacterial strains isolated from different locations within the same stage and between different stages. The results show possibility of multiple entry points of such bacteria entering broiler complex and can potentially contaminate the final raw product in the processing plant. It suggests the need for a comprehensive control strategy with strict biosecurity measures and best management practices to minimize or eliminate such pathogens from the poultry food chain.

Identifying SNP threshold from P2 sequences for investigating norovirus transmission

Noroviruses are a group of non-enveloped single-stranded positive-sense RNA virus belonging to Caliciviridae family. They can be transmitted by the fecal-oral route from contaminated food and water and cause mainly acute gastroenteritis. Outbreaks of norovirus infections could be difficult to detect and investigate. In this study, we developed a simple threshold detection approach based on variations of the P2 domain of the capsid protein. We obtained sequences from the norovirus hypervariable P2 region using Sanger sequencing, including 582 pairs of epidemiologically-related strains from 35 norovirus outbreaks and 6402 pairs of epidemiologically-unrelated strains during the four epidemic seasons. Genetic distances were calculated and a threshold was performed by adopting ROC (Receiver Operating Characteristic) curve which identified transmission clusters in all tested outbreaks with 80 % sensitivity. In average, nucleotide diversity between outbreaks was 67.5 times greater than the diversity within outbreaks. Simple and accurate thresholds for detecting norovirus transmissions of three genotypes obtained here streamlines molecular investigation of norovirus outbreaks, thus enabling rapid and efficient responses for the control of norovirus.

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 diversity of Salmonella enterica isolated from raw chicken at retail establishments in Mexico

The genomic diversity of circulating non-typhoidal Salmonella in raw chicken was investigated in three states of central Mexico. A total of 192 S. enterica strains from chicken meat samples collected at supermarkets, fresh markets, and butcher shops were analyzed by whole-genome sequencing. The serovar distribution, occurrence of genes encoding for antimicrobial resistance, metal resistance, biocide resistance, plasmids and virulence factors, and clonal relatedness based on single nucleotide polymorphism (SNP) analysis were investigated. Serovars Infantis, Schwarzengrund and Enteritidis predominated among twenty identified. The distribution of serovars and proportion of AMR genes was different according to the state, year, season, and retail establishment (p < 0.001). Genes encoding metals resistance were identified in all the strains. A total of 145 virulence genes were identified and strains were classified into 32 virulotypes; serovars Infantis, Typhimurium, and Enteritidis showed the highest number of virulence genes. The strains matched 34 SNP clusters in the NCBI Pathogen Detection server and 59 %, which corresponded to Infantis, Schwarzengrund, Saintpaul, and Enteritidis, were associated with five major clusters and matched with chicken, environmental and clinical isolates from at least three countries. These results provide useful information to understand the epidemiology of Salmonella, conduct microbial risk assessment, and design risk-based control measures.

Using molecular methods to delineate norovirus outbreaks: a systematic review

Noroviruses are among the major causative agents of human acute gastroenteritis, and the nature of norovirus outbreaks can differ considerably. The number of single-nucleotide polymorphisms (SNPs) between strains is used to assess their relationships. There is currently no universally accepted cutoff value for clustering strains that define an outbreak or linking the individuals involved. This study was conducted to estimate the threshold value of genomic variations among related strains within norovirus outbreaks. We carried out a literature search in the PubMed and Web of Science databases. SNP rates were defined as the number of SNPs/sequence length (bp) × 100%. The Mann-Whitney U-test was used in comparisons of the distribution of SNP rates for different sequence regions, genogroups (GI and GII), transmission routes, and sequencing methods. A total of 25 articles reporting on 108 norovirus outbreaks were included. In 99.1% of the outbreaks, the SNP rates were below 0.50%, and in 89.8%, the SNP rates were under 0.20%. Outbreak strains showed higher SNP rates when the P2 domain was used for sequence analysis (Z = -2.652, p = 0.008) and when an NGS method was used (Z = -3.686, p < 0.001). Outbreaks caused by different norovirus genotypes showed no significant difference in SNP rates. Compared with person-to-person outbreaks, SNP rates were lower in common-source outbreaks, but no significant difference was found when differences in sequencing methods were taken into consideraton. SNP rates under 0.20% and 0.50% could be considered as the rigorous and relaxed threshold, respectively, of strain similarity within a norovirus outbreak. More data are needed to evaluate differences within and between various norovirus outbreaks.

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.

Persistent Salmonella enterica serovar Typhi sub-populations within host interrogated by whole genome sequencing and metagenomics

Salmonella enterica serovar Typhi (S. Typhi) causes typhoid fever and, in some cases, chronic carriage after resolution of acute disease. This study examined sequential isolates of S. Typhi from a single host with persistent asymptomatic infection. These isolates, along with another S. Typhi isolate recovered from a household contact with typhoid fever, were subjected to whole genome sequencing and analysis. In addition, direct sequencing of the bile fluid from the host with persistent infection was also performed. Comparative analysis of isolates revealed three sub-populations of S. Typhi with distinct genetic patterns. Metagenomic sequencing recognised only two of the three sub-populations within the bile fluid. The detection and investigation of insertion sequences IS10R and associated deletions complemented analysis of single nucleotide polymorphisms. These findings improve our understanding of within-host dynamics of S. Typhi in cases of persistent infection and inform epidemiological investigations of transmission events associated with chronic carriers.

Economic and health impact modelling of a whole genome sequencing-led intervention strategy for bacterial healthcare-associated infections for England and for the USA

Bacterial healthcare-associated infections (HAIs) are a substantial source of global morbidity and mortality. The estimated cost associated with HAIs ranges from $35 to $45 billion in the USA alone. The costs and accessibility of whole genome sequencing (WGS) of bacteria and the lack of sufficiently accurate, high-resolution, scalable and accessible analysis for strain identification are being addressed. Thus, it is timely to determine the economic viability and impact of routine diagnostic bacterial genomics. The aim of this study was to model the economic impact of a WGS surveillance system that proactively detects and directs interventions for nosocomial infections and outbreaks compared to the current standard of care, without WGS. Using a synthesis of published models, inputs from national statistics, and peer-reviewed articles, the economic impacts of conducting a WGS-led surveillance system addressing the 11 most common nosocomial pathogen groups in England and the USA were modelled. This was followed by a series of sensitivity analyses. England was used to establish the baseline model because of the greater availability of underpinning data, and this was then modified using USA-specific parameters where available. The model for the NHS in England shows bacterial HAIs currently cost the NHS around £3 billion. WGS-based surveillance delivery is predicted to cost £61.1 million associated with the prevention of 74 408 HAIs and 1257 deaths. The net cost saving was £478.3 million, of which £65.8 million were from directly incurred savings (antibiotics, consumables, etc.) and £412.5 million from opportunity cost savings due to re-allocation of hospital beds and healthcare professionals. The USA model indicates that the bacterial HAI care baseline costs are around $18.3 billion. WGS surveillance costs $169.2 million, and resulted in a net saving of ca.$3.2 billion, while preventing 169 260 HAIs and 4862 deaths. From a 'return on investment' perspective, the model predicts a return to the hospitals of £7.83 per £1 invested in diagnostic WGS in the UK, and US$18.74 per $1 in the USA. Sensitivity analyses show that substantial savings are retained when inputs to the model are varied within a wide range of upper and lower limits. Modelling a proactive WGS system addressing HAI pathogens shows significant improvement in morbidity and mortality while simultaneously achieving substantial savings to healthcare facilities that more than offset the cost of implementing diagnostic genomics surveillance.

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

BACKGROUND

Epidemiological surveillance relies on microbial strain typing, which defines genomic relatedness among isolates to identify case clusters and their potential sources. Although predefined thresholds are often applied, known outbreak-specific features such as pathogen mutation rate and duration of source contamination are rarely considered. We aimed to develop a hypothesis-based model that estimates genetic distance thresholds and mutation rates for point-source single-strain food or environmental outbreaks.

METHODS

In this modelling study, we developed a forward model to simulate bacterial evolution at a specific mutation rate (μ) over a defined outbreak duration (D). From the distribution of genetic distances expected under the given outbreak parameters and sample isolation dates, we estimated a distance threshold beyond which isolates should not be considered as part of the outbreak. We embedded the model into a Markov Chain Monte Carlo inference framework to estimate the most probable mutation rate or time since source contamination, which are both often imprecisely documented. A simulation study validated the model over realistic durations and mutation rates. We then identified and analysed 16 published datasets of bacterial source-related outbreaks; datasets were included if they were from an identified foodborne outbreak and if whole-genome sequence data and collection dates for the described isolates were available.

FINDINGS

Analysis of simulated data validated the accuracy of our framework in both discriminating between outbreak and non-outbreak cases and estimating the parameters D and μ from outbreak data. Precision of estimation was much higher for high values of D and μ. Sensitivity of outbreak cases was always very high, and specificity in detecting non-outbreak cases was poor for low mutation rates. For 14 of the 16 outbreaks, the classification of isolates as being outbreak-related or sporadic is consistent with the original dataset. Four of these outbreaks included outliers, which were correctly classified as being beyond the threshold of exclusion estimated by our model, except for one isolate of outbreak 4. For two outbreaks, both foodborne Listeria monocytogenes, conclusions from our model were discordant with published results: in one outbreak two isolates were classified as outliers by our model and in another outbreak our algorithm separated food samples into one cluster and human samples into another, whereas the isolates were initially grouped together based on epidemiological and genetic evidence. Re-estimated values of the duration of outbreak or mutation rate were largely consistent with a priori defined values. However, in several cases the estimated values were higher and improved the fit with the observed genetic distance distribution, suggesting that early outbreak cases are sometimes missed.

INTERPRETATION

We propose here an evolutionary approach to the single-strain conundrum by estimating the genetic threshold and proposing the most probable cluster of cases for a given outbreak, as determined by its particular epidemiological and microbiological properties. This forward model, applicable to foodborne or environmental-source single point case clusters or outbreaks, is useful for epidemiological surveillance and may inform control measures.

FUNDING

European Union Horizon 2020 Research and Innovation Programme.

MGTdb: a web service and database for studying the global and local genomic epidemiology of bacterial pathogens

Multilevel genome typing (MGT) enables the genomic characterization of bacterial isolates and the relationships among them. The MGT system describes an isolate using multiple multilocus sequence typing (MLST) schemes, referred to as levels. Thus, for a new isolate, sequence types (STs) assigned at multiple precisely defined levels can be used to type isolates at multiple resolutions. The MGT designation for isolates is stable, and the assignment is faster than the existing approaches. MGT’s utility has been demonstrated in multiple species. This paper presents a publicly accessible web service called MGTdb, which enables the assignment of MGT STs to isolates, along with their storage, retrieval and analysis. The MGTdb web service enables upload of genome data as sequence reads or alleles, which are processed and assigned MGT identifiers. Additionally, any newly sequenced isolates deposited in the National Center for Biotechnology Information’s Sequence Read Archive are also regularly retrieved (currently daily), processed, assigned MGT identifiers and made publicly available in MGTdb. Interactive visualization tools are presented to assist analysis, along with capabilities to download publicly available isolates and assignments for use with external software. MGTdb is currently available for Salmonella enterica serovars Typhimurium and Enteritidis and Vibrio cholerae. We demonstrate the usability of MGTdb through three case studies — to study the long-term national surveillance of S. Typhimurium, the local epidemiology and outbreaks of S. Typhimurium, and the global epidemiology of V. cholerae. Thus, MGTdb enables epidemiological and microbiological investigations at multiple levels of resolution for all publicly available isolates of these pathogens.

Database URL: https://mgtdb.unsw.edu.au

Molecular epidemiology characteristics and detecting transmission of carbapenemase-producing enterobacterales in southwestern China

BACKGROUND

To investigate the genotype and clinical characteristics of carbapenem-resistant Enterobacteriaceae (CRE) strains in southwest China and provide information on the treatment stopping the spread of the infection.

METHODS

The clinical information of CRE isolates was collected from 19 hospitals in 12 cities across Sichuan Province, China, between June 2018 and April 2019. The isolates were detected by DNA sequencing of genes encoding carbapenem enzymes and multilocus sequence types (MLSTs).

RESULTS

A total of 166 nonrepetitive CRE isolates were isolated during the study period from sputum, blood, urine, and other samples. Klebsiella pneumoniae carbapenemase (KPC) was dominant in Klebsiella pneumoniae (53.9%), followed by New Delhi metallo-β-lactamase (NDM) (42.1%). A total of 43 STs were detected. The most common ST of K. pneumoniae was ST11, and that of Escherichia coli was ST410. Pairwise single nucleotide polymorphism (SNP) distances and the likelihood of local transmission by epidemiology were plotted for each species. About 65% of these pairs had ≤ 20 pairwise SNPs.

CONCLUSION

A large number of CRE strains carried carbapenemase. Although NDM-ST12 K. pneumoniae should not be disregarded, KPC-ST11is the predominant strain. Thus, the possibility of transmission between E. coli and K. pneumoniae could not be ignored.

Genomic evidence of environmental and resident Salmonella Senftenberg and Montevideo contamination in the pistachio supply-chain

Pistachios have been implicated in two salmonellosis outbreaks and multiple recalls in the U.S. This study performed an in-depth retrospective data analysis of Salmonella associated with pistachios as well as a storage study to evaluate the survivability of Salmonella on inoculated inshell pistachios to further understand the genetics and microbiological dynamics of this commodity-pathogen pair. The retrospective data analysis on isolates associated with pistachios was performed utilizing short-read and long-read sequencing technologies. The sequence data were analyzed using two methods: the FDA's Center for Food Safety and Applied Nutrition Single Nucleotide Polymorphism (SNP) analysis and Whole Genome Multilocus Sequence Typing (wgMLST). The year-long storage study evaluated the survival of five strains of Salmonella on pistachios stored at 25 °C at 35% and 54% relative humidity (RH). Our results demonstrate: i) evidence of persistent Salmonella Senftenberg and Salmonella Montevideo strains in pistachio environments, some of which may be due to clonal resident strains and some of which may be due to preharvest contamination; ii) presence of the Copper Homeostasis and Silver Resistance Island (CHASRI) in Salmonella Senftenberg and Montevideo strains in the pistachio supply chain; and iii) the use of metagenomic analysis is a novel tool for determining the composition of serovar survival in a cocktail inoculated storage study.

Enhancing genomics-based outbreak detection of endemic Salmonella enterica serovar Typhimurium using dynamic thresholds

Salmonella enterica serovar Typhimurium is the leading cause of salmonellosis in Australia, and the ability to identify outbreaks and their sources is vital to public health. Here, we examined the utility of whole-genome sequencing (WGS), including complete genome sequencing with Oxford Nanopore technologies, in examining 105 isolates from an endemic multi-locus variable number tandem repeat analysis (MLVA) type over 5 years. The MLVA type was very homogeneous, with 90 % of the isolates falling into groups with a five SNP cut-off. We developed a new two-step approach for outbreak detection using WGS. The first clustering at a zero single nucleotide polymorphism (SNP) cut-off was used to detect outbreak clusters that each occurred within a 4 week window and then a second clustering with dynamically increased SNP cut-offs were used to generate outbreak investigation clusters capable of identifying all outbreak cases. This approach offered optimal specificity and sensitivity for outbreak detection and investigation, in particular of those caused by endemic MLVA types or clones with low genetic diversity. We further showed that inclusion of complete genome sequences detected no additional mutational events for genomic outbreak surveillance. Phylogenetic analysis found that the MLVA type was likely to have been derived recently from a single source that persisted over 5 years, and seeded numerous sporadic infections and outbreaks. Our findings suggest that SNP cut-offs for outbreak cluster detection and public-health surveillance should be based on the local diversity of the relevant strains over time. These findings have general applicability to outbreak detection of bacterial pathogens.

Genomic Characterization of Salmonella typhimurium DT104 Strains Associated with Cattle and Beef Products

Salmonella enterica subsp. enterica serovar Typhimurium DT104, a multidrug-resistant phage type, has emerged globally as a major cause of foodborne outbreaks particularly associated with contaminated beef products. In this study, we sequenced three S. Typhimurium DT104 strains associated with a 2009 outbreak caused by ground beef, including the outbreak source strain and two clinical strains. The goal of the study was to gain a stronger understanding of the genomics and genomic epidemiology of highly clonal S. typhimurium DT104 strains associated with bovine sources. Our study found no single nucleotide polymorphisms (SNPs) between the ground beef source strain and the clinical isolates from the 2009 outbreak. SNP analysis including twelve other S. typhimurium strains from bovine and clinical sources, including both DT104 and non-DT104, determined DT104 strains averaged 55.0 SNPs between strains compared to 474.5 SNPs among non-DT104 strains. Phylogenetic analysis separated the DT104 strains from the non-DT104 strains, but strains did not cluster together based on source of isolation even within the DT104 phage type. Pangenome analysis of the strains confirmed previous studies showing that DT104 strains are missing the genes for the allantoin utilization pathway, but this study confirmed that the genes were part of a deletion event and not substituted or disrupted by the insertion of another genomic element. Additionally, cgMLST analysis revealed that DT104 strains with cattle as the source of isolation were quite diverse as a group and did not cluster together, even among strains from the same country. Expansion of the analysis to 775 S. typhimurium ST19 strains associated with cattle from North America revealed diversity between strains, not limited to just among DT104 strains, which suggests that the cattle environment is favorable for a diverse group of S. typhimurium strains and not just DT104 strains.

Genomic diversity of Salmonella enterica isolated from papaya samples collected during multiple outbreaks in 2017

In 2017, the US Food and Drug Administration investigated the sources of multiple outbreaks of salmonellosis. Epidemiologic and traceback investigations identified Maradol papayas as the suspect vehicles. During the investigations, the genomes of 55 Salmonella enterica that were isolated from papaya samples were sequenced. Serovar assignments and phylogenetic analysis placed the 55 isolates into ten distinct groups, each representing a different serovar. Within-serovar SNP differences are generally between 0 and 20 SNPs, while the median between-serovar distance is 51 812 SNPs. We observed two groups with SNP distances between 21 and 100 SNPs. These relatively large within-serovar SNP distances may indicate that the isolates represent either diverse populations or multiple, genetically distinct subpopulations. Further inspection of these cases with traceback evidence allowed us to identify an 11th population. We observed that high levels of genomic diversity from individual firms is possible, with one firm yielding five of the ten serovars. Also, high levels of diversity are possible within small geographic regions, as five of the serovars were isolated from papayas that originated from farms located in Armería and Tecomán, Colima. In addition, we identified AMR genes that are present in three of the serovars studied here (aph(3')-lb, aph(6)-ld, tet(C), fosA7, and qnrB19) and we detected the presence of the plasmid IncHI2A among S. Urbana isolates.

Genetic changes are introduced by repeated exposure of Salmonella spiked in low water activity and high fat matrix to heat

WGS is used to define if isolates are "in" or "out" of an outbreak and/or microbial root cause investigation. No threshold of genetic differences is fixed and the conclusions on similarity between isolates are mainly based on the knowledge generated from previous outbreak investigations and reported mutation rates. Mutation rates in Salmonella when exposed to food processing conditions are lacking. Thus, in this study, the ability of heat and dry stress to cause genetic changes in two Salmonella serotypes frequently isolated from low moisture foods was investigated. S. enterica serovars S. Agona ATCC 51,957 and S. Mbandaka NCTC 7892 (ATCC 51,958) were repeatedly exposed to heat (90 °C for 5 min) in a low water activity and high fat matrix. No increased fitness of the strains was observed after 10 repeated heat treatments. However, genetic changes were introduced and the number of genetic differences increased with every heat treatment cycle. The genetic changes appeared randomly in the genome and were responsible for a population of diverse isolates with 0 to 28 allelic differences (0 to 38 SNPs) between them. This knowledge is key to interpret WGS results for source tracking investigations as part of a root cause analysis in a contamination event as isolates are exposed to stress conditions.

Budget impact analysis of routinely using whole-genomic sequencing of six multidrug-resistant bacterial pathogens in Queensland, Australia

OBJECTIVE

To predict the cost and health effects of routine use of whole-genome sequencing (WGS) of bacterial pathogens compared with those of standard of care.

DESIGN

Budget impact analysis was performed over the following 5 years. Data were primarily from sequencing results on clusters of multidrug-resistant organisms across 27 hospitals. Model inputs were derived from hospitalisation and sequencing data, and epidemiological and costing reports, and included multidrug resistance rates and their trends.

SETTING

Queensland, Australia.

PARTICIPANTS

Hospitalised patients.

INTERVENTIONS

WGS surveillance of six common multidrug-resistant organisms (Staphylococcus aureus, Escherichia coli, Enterococcus faecium, Klebsiella pneumoniae, Enterobacter sp and Acinetobacter baumannii) compared with standard of care or routine microbiology testing.

PRIMARY AND SECONDARY OUTCOMES

Expected hospital costs, counts of patient infections and colonisations, and deaths from bloodstream infections.

RESULTS

In 2021, 97 539 patients in Queensland are expected to be infected or colonised with one of six multidrug-resistant organisms with standard of care testing. WGS surveillance strategy and earlier infection control measures could avoid 36 726 infected or colonised patients and avoid 650 deaths. The total cost under standard of care was $A170.8 million in 2021. WGS surveillance costs an additional $A26.8 million but was offset by fewer costs for cleaning, nursing, personal protective equipment, shorter hospital stays and antimicrobials to produce an overall cost savings of $30.9 million in 2021. Sensitivity analyses showed cost savings remained when input values were varied at 95% confidence limits.

CONCLUSIONS

Compared with standard of care, WGS surveillance at a state-wide level could prevent a substantial number of hospital patients infected with multidrug-resistant organisms and related deaths and save healthcare costs. Primary prevention through routine use of WGS is an investment priority for the control of serious hospital-associated infections.

Cost-effectiveness analysis of whole-genome sequencing during an outbreak of carbapenem-resistant Acinetobacter baumannii

Background:

Whole-genome sequencing (WGS) shotgun metagenomics (metagenomics) attempts to sequence the entire genetic content straight from the sample. Diagnostic advantages lie in the ability to detect unsuspected, uncultivatable, or very slow-growing organisms.

Objective:

To evaluate the clinical and economic effects of using WGS and metagenomics for outbreak management in a large metropolitan hospital.

Design:

Cost-effectiveness study.

Setting:

Intensive care unit and burn unit of large metropolitan hospital.

Patients:

Simulated intensive care unit and burn unit patients.

Methods:

We built a complex simulation model to estimate pathogen transmission, associated hospital costs, and quality-adjusted life years (QALYs) during a 32-month outbreak of carbapenem-resistant Acinetobacter baumannii (CRAB). Model parameters were determined using microbiology surveillance data, genome sequencing results, hospital admission databases, and local clinical knowledge. The model was calibrated to the actual pathogen spread within the intensive care unit and burn unit (scenario 1) and compared with early use of WGS (scenario 2) and early use of WGS and metagenomics (scenario 3) to determine their respective cost-effectiveness. Sensitivity analyses were performed to address model uncertainty.

Results:

On average compared with scenario 1, scenario 2 resulted in 14 fewer patients with CRAB, 59 additional QALYs, and $75,099 cost savings. Scenario 3, compared with scenario 1, resulted in 18 fewer patients with CRAB, 74 additional QALYs, and $93,822 in hospital cost savings. The likelihoods that scenario 2 and scenario 3 were cost-effective were 57% and 60%, respectively.

Conclusions:

The use of WGS and metagenomics in infection control processes were predicted to produce favorable economic and clinical outcomes.

Multilevel genome typing: genomics-guided scalable resolution typing of microbial pathogens

Background

Both long- and short-term epidemiology are fundamental to disease control and require accurate bacterial typing. Genomic data resulting from implementation of whole genome sequencing in many public health laboratories can potentially provide highly sensitive and accurate descriptions of strain relatedness. Previous typing efforts using these data have mainly focussed on outbreak detection.

Aim

We aimed to develop multilevel genome typing (MGT), using consecutive multilocus sequence typing (MLST) schemes of increasing sizes, stepping up from seven-gene MLST to core genome MLST, to allow examination of genetic relatedness at multiple resolution levels.

Methods

The system was applied to Salmonellaenterica serovar Typhimurium. The MLST scheme used at each step (MGT level), defined a given MGT-level specific sequence type (ST). The list of STs generated from all of these increasing MGT levels, was named a genome type (GT). Using MGT, we typed 9,096 previously characterised isolates with publicly available data.

Results

Our approach could identify previously described S. Typhimurium populations, such as the DT104 multidrug resistance lineage (GT 19-2-11) and two invasive lineages of African isolates (GT 313-2-3 and 313-2-752). Further, we showed that MGT-derived clusters can accurately distinguish five outbreaks from each other and five background isolates.

Conclusion

MGT provides a universal and stable nomenclature at multiple resolutions for S. Typhimurium strains and could be implemented as an internationally standardised strain identification system. While established so far only for S. Typhimurium, the results here suggest that MGT could form the basis for typing systems in other similar microorganisms.

Diverse lineages of multidrug resistant clinical Salmonella enterica and a cryptic outbreak in New Hampshire, USA revealed from a year-long genomic surveillance

Salmonella enterica, the causative agent of gastrointestinal diseases and typhoid fever, is a human and animal pathogen that causes significant mortality and morbidity worldwide. In this study, we examine the genomic diversity and phylogenetic relationships of 63 S. enterica isolates from human-derived clinical specimens submitted to the Department of Health and Human Services (DHHS) in the state of New Hampshire, USA in 2017. We found a remarkably large genomic, phylogenetic and serotype variation among the S. enterica isolates, dominated by serotypes Enteritidis (sequence type [ST] 11), Heidelberg (ST 15) and Typhimurium (ST 19). Analysis of the distribution of single nucleotide polymorphisms in the core genome suggests that the ST 15 cluster is likely a previously undetected or cryptic outbreak event that occurred in the south/southeastern part of New Hampshire in August-September. We found that nearly all of the clinical S. enterica isolates carried horizontally acquired genes that confer resistance to multiple classes of antimicrobials, most notably aminoglycosides, fluoroquinolones and macrolides. Majority of the isolates (76.2%) carry at least four resistance determinants per genome. We also detected the genes mdtK and mdsABC that encode multidrug efflux pumps and the gene sdiA that encodes a regulator for a third multidrug resistance pump. Our results indicate rapid microevolution and geographical dissemination of multidrug resistant lineages over a short time span. These findings are critical to aid the DHHS and similar public health laboratories in the development of effective disease control measures, epidemiological studies and treatment options for serious Salmonella infections.

Comparison of conventional molecular and whole-genome sequencing methods for subtyping Salmonella enterica serovar Enteritidis strains from Tunisia

We sought to determine the relative value of conventional molecular methods and whole-genome sequencing (WGS) for subtyping Salmonella enterica serovar Enteritidis recovered from 2000 to 2015 in Tunisia and to investigate the genetic diversity of this serotype. A total of 175 Salmonella Enteritidis isolates were recovered from human, animal, and foodborne outbreak samples. Pulsed-field gel electrophoresis (PFGE), multiple locus variable-number tandem repeat analysis (MLVA), and whole-genome sequencing were performed. Eight pulsotypes were detected for all isolates with PFGE (DI = 0.518). Forty-five Salmonella Enteritidis isolates were selected for the MLVA and WGS techniques. Eighteen MLVA profiles were identified and classified into two major clusters (DI = 0.889). Core genome multilocus typing (cgMLST) analysis revealed 16 profiles (DI = 0.785). Whole-genome analysis indicated 660 single-nucleotide polymorphism (SNP) divergences dividing these isolates into 43 haplotypes (DI = 0.997). The phylogenetic tree supported the classification of Salmonella Enteritidis isolates into two distinct lineages subdivided into five clades and seven subclades. Pairwise SNP differences between the isolates ranged between 302 and 350. We observed about 311 SNP differences between the two foodborne outbreaks, while only less or equal to 4 SNP differences within each outbreak. SNP-based WGS typing showed an excellent discriminatory power comparing with the conventional methods such as PFGE and MLVA. Besides, we demonstrate the added value of WGS as a complementary subtyping method to discriminate outbreak from non-outbreak isolates belonging to common subtypes. It is important to continue the survey of Salmonella Enteritidis lineages in Tunisia using WGS.

Salmonella enterica Serovar Hvittingfoss in Bar-Tailed Godwits (Limosa lapponica) from Roebuck Bay, Northwestern Australia

Salmonella enterica serovar Hvittingfoss is an important foodborne serotype of Salmonella, being detected in many countries where surveillance is conducted. Outbreaks can occur, and there was a recent multistate foodborne outbreak in Australia. S Hvittingfoss can be found in animal populations, though a definitive animal host has not been established. Six species of birds were sampled at Roebuck Bay, a designated Ramsar site in northwestern Australia, resulting in 326 cloacal swabs for bacterial culture. Among a single flock of 63 bar-tailed godwits (Limosa lapponica menzbieri) caught at Wader Spit, Roebuck Bay, in 2018, 17 (27%) were culture positive for Salmonella All other birds were negative for Salmonella The isolates were identified as Salmonella enterica serovar Hvittingfoss. Phylogenetic analysis revealed a close relationship between isolates collected from godwits and the S Hvittingfoss strain responsible for a 2016 multistate foodborne outbreak originating from tainted cantaloupes (rock melons) in Australia. While it is not possible to determine how this strain of S Hvittingfoss was introduced into the bar-tailed godwits, these findings show that wild Australian birds are capable of carrying Salmonella strains of public health importance.IMPORTANCE Salmonella is a zoonotic pathogen that causes gastroenteritis and other disease presentations in both humans and animals. Serovars of S. enterica commonly cause foodborne disease in Australia and globally. In 2016-2017, S Hvittingfoss was responsible for an outbreak that resulted in 110 clinically confirmed human cases throughout Australia. The origin of the contamination that led to the outbreak was never definitively established. Here, we identify a migratory shorebird, the bar-tailed godwit, as an animal reservoir of S Hvittingfoss. These birds were sampled in northwestern Australia during their nonbreeding period. The presence of a genetically similar S Hvittingfoss strain circulating in a wild bird population, 2 years after the 2016-2017 outbreak and ∼1,500 km from the suspected source of the outbreak, demonstrates a potentially unidentified environmental reservoir of S Hvittingfoss. While the birds cannot be implicated in the outbreak that occurred 2 years prior, this study does demonstrate the potential role for wild birds in the transmission of this important foodborne pathogen.

In vivo passage of Salmonella Typhimurium results in minor mutations in the bacterial genome and increases in vitro invasiveness

Eggs and raw or undercooked egg-containing food items are frequently identified as the bacterial source during epidemiolocal investigation of Salmonella outbreaks. Multi-locus variable number of tandem repeats analysis (MLVA) is a widely used Salmonella typing method enabling the study of diversity within populations of the same serotype. In vivo passage, however, has been linked with changes in MLVA type and more broadly the Salmonella genome. We sought to investigate whether in vivo passage through layer hens had an effect on MLVA type as well as the bacterial genome and whether any mutations affected bacterial virulence. Layer hens were infected with either Salmonella Typhimurium DT9 (03-24-11-11-523) as part of a single infection or were co-infected with an equal amount of Salmonella Mbandaka. Salmonella shedding in both single and co-infected birds was variable over the course of the 16-week experiment. Salmonella Typhimurium and Salmonella Mbandaka were identified in feces of co-infected birds. Salmonella colonies isolated from fecal samples were subtyped using MLVA. A single change in SSTR-6 was observed in Salmonella Typhimurium strains isolated from co-infected birds. Isolates of Salmonella Typhimurium of both the parent (03-24-11-11-523) and modified (03-24-12-11-523) MLVA type were sequenced and compared with the genome of the parent strain. Sequence analysis revealed that in vivo passaging resulted in minor mutation events. Passaged isolates exhibited significantly higher invasiveness in cultured human intestinal epithelial cells than the parent strain. The microevolution observed in this study suggests that changes in MLVA may arise more commonly and may have clinical significance.

Rise and fall of outbreak-specific clone inside endemic pulsotype of Salmonella 4,[5],12:i:-; insights from high-resolution molecular surveillance in Emilia-Romagna, Italy, 2012 to 2015

Background and aimEpidemiology of human non-typhoid salmonellosis is characterised by recurrent emergence of new clones of the pathogen over time. Some clonal lines of Salmonella have shaped epidemiology of the disease at global level, as happened for serotype Enteritidis or, more recently, for Salmonella 4,[5],12:i:-, a monophasic variant of serotype Typhimurium. The same clonal behaviour is recognisable at sub-serotype level where single outbreaks or more generalised epidemics are attributable to defined clones. The aim of this study was to understand the dynamics of a clone of Salmonella 4,[5],12:i:- over a 3-year period (2012-15) in a province of Northern Italy where the clone caused a large outbreak in 2013. Furthermore, the role of candidate outbreak sources was investigated and the accuracy of multilocus variable-number tandem repeat analysis (MLVA) was evaluated. Methods: we retrospectively investigated the outbreak through whole genome sequencing (WGS) and further monitored the outbreak clone for 2 years after its conclusion. Results: The study showed the transient nature of the clone in the population, possibly as a consequence of its occasional expansion in a food-processing facility. We demonstrated that important weaknesses characterise conventional typing methods applied to clonal pathogens such as Salmonella 4,[5],12:i:-, namely lack of accuracy for MLVA and inadequate resolution power for PFGE to be reliably used for clone tracking. Conclusions: The study provided evidence for the remarkable prevention potential of whole genome sequencing used as a routine tool in systems that integrate human, food and animal surveillance.

Genomics for Molecular Epidemiology and Detecting Transmission of Carbapenemase-Producing Enterobacterales in Victoria, Australia, 2012 to 2016

Carbapenemase-producing Enterobacterales (CPE) are being increasingly reported in Australia, and integrated clinical and genomic surveillance is critical to effectively manage this threat. We sought to systematically characterize CPE in Victoria, Australia, from 2012 to 2016.

Carbapenemase-producing Enterobacterales (CPE) are being increasingly reported in Australia, and integrated clinical and genomic surveillance is critical to effectively manage this threat. We sought to systematically characterize CPE in Victoria, Australia, from 2012 to 2016. Suspected CPE were referred to the state public health laboratory in Victoria, Australia, from 2012 to 2016 and examined using phenotypic, multiplex PCR and whole-genome sequencing (WGS) methods and compared with epidemiological metadata. Carbapenemase genes were detected in 361 isolates from 291 patients (30.8% of suspected CPE isolates), mostly from urine (42.1%) or screening samples (34.8%). IMP-4 (28.0% of patients), KPC-2 (25.3%), NDM (24.1%), and OXA carbapenemases (22.0%) were most common. Klebsiella pneumoniae (48.8% of patients) and Escherichia coli (26.1%) were the dominant species. Carbapenemase-inactivation method (CIM) testing reliably detected carbapenemase-positive isolates (100% sensitivity, 96.9% specificity), identifying an additional five CPE among 159 PCR-negative isolates (IMI and SME carbapenemases). When epidemiologic investigations were performed, all pairs of patients designated “highly likely” or “possible” local transmission had ≤23 pairwise single-nucleotide polymorphisms (SNPs) by genomic transmission analysis; conversely, all patient pairs designated “highly unlikely” local transmission had ≥26 pairwise SNPs. Using this proposed threshold, possible local transmission was identified involving a further 16 patients for whom epidemiologic data were unavailable. Systematic application of genomics has uncovered the emergence of polyclonal CPE as a significant threat in Australia, providing important insights to inform local public health guidelines and interventions. Using our workflow, pairwise SNP distances between CPE isolates of ≤23 SNPs suggest local transmission.

Assessment and Comparison of Molecular Subtyping and Characterization Methods for Salmonella

The food industry is facing a major transition regarding methods for confirmation, characterization, and subtyping of Salmonella. Whole-genome sequencing (WGS) is rapidly becoming both the method of choice and the gold standard for Salmonella subtyping; however, routine use of WGS by the food industry is often not feasible due to cost constraints or the need for rapid results. To facilitate selection of subtyping methods by the food industry, we present: (i) a comparison between classical serotyping and selected widely used molecular-based subtyping methods including pulsed-field gel electrophoresis, multilocus sequence typing, and WGS (including WGS-based serovar prediction) and (ii) a scoring system to evaluate and compare Salmonella subtyping assays. This literature-based assessment supports the superior discriminatory power of WGS for source tracking and root cause elimination in food safety incident; however, circumstances in which use of other subtyping methods may be warranted were also identified. This review provides practical guidance for the food industry and presents a starting point for further comparative evaluation of Salmonella characterization and subtyping methods.

Beyond the SNP Threshold: Identifying Outbreak Clusters Using Inferred Transmissions

Whole-genome sequencing (WGS) is increasingly used to aid the understanding of pathogen transmission. A first step in analyzing WGS data is usually to define "transmission clusters," sets of cases that are potentially linked by direct transmission. This is often done by including two cases in the same cluster if they are separated by fewer single-nucleotide polymorphisms (SNPs) than a specified threshold. However, there is little agreement as to what an appropriate threshold should be. We propose a probabilistic alternative, suggesting that the key inferential target for transmission clusters is the number of transmissions separating cases. We characterize this by combining the number of SNP differences and the length of time over which those differences have accumulated, using information about case timing, molecular clock, and transmission processes. Our framework has the advantage of allowing for variable mutation rates across the genome and can incorporate other epidemiological data. We use two tuberculosis studies to illustrate the impact of our approach: with British Columbia data by using spatial divisions; with Republic of Moldova data by incorporating antibiotic resistance. Simulation results indicate that our transmission-based method is better in identifying direct transmissions than a SNP threshold, with dissimilarity between clusterings of on average 0.27 bits compared with 0.37 bits for the SNP-threshold method and 0.84 bits for randomly permuted data. These results show that it is likely to outperform the SNP-threshold method where clock rates are variable and sample collection times are spread out. We implement the method in the R package transcluster.

Differentiating Epidemic from Endemic or Sporadic Infectious Disease Occurrence

One important scope of work of epidemiology is the investigation of infectious diseases that cluster in time and place. Clusters of infectious disease may represent outbreaks or epidemics in which the cases share in common a point source exposure or an infectious agent in a chain of transmission pathways. Investigations of outbreaks of an illness can facilitate identification of a source, risk, or cause of the illness. However, most infectious disease episodes occur not as part of any apparent outbreaks but as sporadic infections. Multiple sporadic infections that occur steadily in time and place are referred to as endemic disease. How does one investigate sources and risk factors for sporadic or endemic infections? As part of the Microbiology Spectrum Curated Collection: Advances in Molecular Epidemiology of Infectious Diseases, this review discusses limitations of traditional approaches and advantages of molecular epidemiology approaches to investigate sporadic and endemic infections. Using specific examples, the discussions show that most sporadic infections are actually part of unrecognized outbreaks and that what appears to be endemic disease occurrence is actually comprised of multiple small outbreaks. These molecular epidemiologic investigations have unmasked modes of transmission of infectious agents not known to cause outbreaks. They have also raised questions about the traditional ways to measure incidence and assess sources of drug-resistant infections in community settings. The discoveries made by the application of molecular microbiology methods in epidemiologic investigations have led to creation of new public health intervention strategies that have not been previously considered. *This article is part of a curated collection.

Comparative genomic analysis unravels the transmission pattern and intra-species divergence of acute hepatopancreatic necrosis disease (AHPND)-causing Vibrio parahaemolyticus strains

Acute hepatopancreatic necrosis disease (AHPND) is a recently discovered shrimp disease that has become a severe threat to global shrimp-farming industry. The causing agents of AHPND were identified as Vibrio parahaemolyticus and other vibrios harboring a plasmid encoding binary toxins PirA^vp/PirB^vp. However, the epidemiological involvement of environmental vibrios in AHPND is poorly understood. In this study, with an aim to reveal the possible transmission route of AHPND-causing V. parahaemolyticus, we sequenced and analyzed the genomes of four pairs of V. parahaemolyticus strains from four representative regions of shrimp farming in China, each including one strain isolated from diseased shrimp during an AHPND outbreak and one strain isolated from sediment before AHPND outbreaks. Our results showed that all the four shrimp-isolated and three of the sediment-isolated strains encode and secret PirA^vp/PirB^vp toxins and, therefore, are AHPND-causing strains. In silico multilocus sequence typing and high-resolution phylogenomic analysis based on single-nucleotide polymorphisms, as well as comparison of genomic loci in association with prophages and capsular polysaccharides (CPSs) consistently pointed to a close genetic relationship between the shrimp- and sediment-isolated strains obtained from the same region. In addition, our analyses revealed that the sequences associated with prophages, CPSs, and type VI secretion system-1 are highly divergent among strains from different regions, implying that these genes may play vital roles in environmental adaptation for AHPND-causing V. parahaemolyticus and thereby be potential targets for AHPND control. Summing up, this study provides the first direct evidence regarding the transmission route of AHPND-causing V. parahaemolyticus and underscores that V. parahaemolyticus in shrimp are most likely originated from local environment. The importance of environmental disinfection measures in shrimp farming was highlighted.

GenomeGraphR: A user-friendly open-source web application for foodborne pathogen whole genome sequencing data integration, analysis, and visualization

Food safety risk assessments and large-scale epidemiological investigations have the potential to provide better and new types of information when whole genome sequence (WGS) data are effectively integrated. Today, the NCBI Pathogen Detection database WGS collections have grown significantly through improvements in technology, coordination, and collaboration, such as the GenomeTrakr and PulseNet networks. However, high-quality genomic data is not often coupled with high-quality epidemiological or food chain metadata. We have created a set of tools for cleaning, curation, integration, analysis and visualization of microbial genome sequencing data. It has been tested using Salmonella enterica and Listeria monocytogenes data sets provided by NCBI Pathogen Detection (160,000 sequenced isolates in 2018). GenomeGraphR presents foodborne pathogen WGS data and associated curated metadata in a user-friendly interface that allows a user to query a variety of research questions such as, transmission sources and dynamics, global reach, and persistence of genotypes associated with contamination in the food supply and foodborne illness across time or space. The application is freely available (https://fda-riskmodels.foodrisk.org/genomegraphr/).

Retrospective whole-genome sequencing analysis distinguished PFGE and drug-resistance-matched retail meat and clinical Salmonella isolates

Non-typhoidal Salmonella is a leading cause of outbreak and sporadic-associated foodborne illnesses in the United States. These infections have been associated with a range of foods, including retail meats. Traditionally, pulsed-field gel electrophoresis (PFGE) and antibiotic susceptibility testing (AST) have been used to facilitate public health investigations of Salmonella infections. However, whole-genome sequencing (WGS) has emerged as an alternative tool that can be routinely implemented. To assess its potential in enhancing integrated surveillance in Pennsylvania, USA, WGS was used to directly compare the genetic characteristics of 7 retail meat and 43 clinical historic Salmonella isolates, subdivided into 3 subsets based on PFGE and AST results, to retrospectively resolve their genetic relatedness and identify antimicrobial resistance (AMR) determinants. Single nucleotide polymorphism (SNP) analyses revealed that the retail meat isolates within S. Heidelberg, S. Typhimurium var. O5- subset 1 and S. Typhimurium var. O5- subset 2 were separated from each primary PFGE pattern-matched clinical isolate by 6-12, 41-96 and 21-81 SNPs, respectively. Fifteen resistance genes were identified across all isolates, including fosA7, a gene only recently found in a limited number of Salmonella and a ≥95 % phenotype to genotype correlation was observed for all tested antimicrobials. Moreover, AMR was primarily plasmid-mediated in S. Heidelberg and S. Typhimurium var. O5- subset 2, whereas AMR was chromosomally carried in S. Typhimurium var. O5- subset 1. Similar plasmids were identified in both the retail meat and clinical isolates. Collectively, these data highlight the utility of WGS in retrospective analyses and enhancing integrated surveillance for Salmonella from multiple sources.

Interpreting Whole-Genome Sequence Analyses of Foodborne Bacteria for Regulatory Applications and Outbreak Investigations

Whole-genome sequence (WGS) analysis has revolutionized the food safety industry by enabling high-resolution typing of foodborne bacteria. Higher resolving power allows investigators to identify origins of contamination during illness outbreaks and regulatory activities quickly and accurately. Government agencies and industry stakeholders worldwide are now analyzing WGS data routinely. Although researchers have published many studies that assess the efficacy of WGS data analysis for source attribution, guidance for interpreting WGS analyses is lacking. Here, we provide the framework for interpreting WGS analyses used by the Food and Drug Administration's Center for Food Safety and Applied Nutrition (CFSAN). We based this framework on the experiences of CFSAN investigators, collaborations and interactions with government and industry partners, and evaluation of the published literature. A fundamental question for investigators is whether two or more bacteria arose from the same source of contamination. Analysts often count the numbers of nucleotide differences [single-nucleotide polymorphisms (SNPs)] between two or more genome sequences to measure genetic distances. However, using SNP thresholds alone to assess whether bacteria originated from the same source can be misleading. Bacteria that are isolated from food, environmental, or clinical samples are representatives of bacterial populations. These populations are subject to evolutionary forces that can change genome sequences. Therefore, interpreting WGS analyses of foodborne bacteria requires a more sophisticated approach. Here, we present a framework for interpreting WGS analyses that combines SNP counts with phylogenetic tree topologies and bootstrap support. We also clarify the roles of WGS, epidemiological, traceback, and other evidence in forming the conclusions of investigations. Finally, we present examples that illustrate the application of this framework to real-world situations.

Retrospective use of whole genome sequencing to better understand an outbreak of Salmonella enterica serovar Mbandaka in New South Wales, Australia

Introduction

Salmonella enterica serovar Mbandaka is an infrequent cause of salmonellosis in New South Wales (NSW) with an average of 17 cases reported annually. This study examined the added value of whole genome sequencing (WGS) for investigating a non-point source outbreak of Salmonella ser. Mbandaka with limited geographical spread.

Methods

In February 2016, an increase in Salmonella ser. Mbandaka was noted in New South Wales, and an investigation was initiated. A WGS study was conducted three months after the initial investigation, analysing the outbreak Salmonella ser. Mbandaka isolates along with 17 human and non-human reference strains from 2010 to 2015.

Results

WGS analysis distinguished the original outbreak cases (n = 29) into two main clusters: Cluster A (n = 11) and Cluster B (n = 6); there were also 12 sporadic cases. Reanalysis of food consumption histories of cases by WGS cluster provided additional specificity when assessing associations.

Discussion

WGS has been widely acknowledged as a promising high-resolution typing tool for enteric pathogens. This study was one of the first to apply WGS to a geographically limited cluster of salmonellosis in Australia. WGS clearly distinguished the outbreak cases into distinct clusters, demonstrating its potential value for use in real time to support non-point source foodborne disease outbreaks of limited geographical spread.

The role of meat in foodborne disease: Is there a coming revolution in risk assessment and management?

Meat has featured prominently as a source of foodborne disease and a public health concern. For about the past 20 years the risk management paradigm has dominated international thinking about food safety. Control through the supply chain is supported by risk management concepts, as the public health risk at the point of consumption becomes the accepted outcome based measure. Foodborne pathogens can be detected at several points in the supply chain and determining the source of where these pathogens arise and how they behave throughout meat production and processing are important parts of risk based approaches. Recent improvements in molecular and genetic based technologies and data analysis for investigating source attribution and pathogen behaviour have enabled greater insights into how foodborne outbreaks occur and where controls can be implemented. These new approaches will improve our understanding of the role of meat in foodborne disease and are expected to have a significant impact on our understanding in the coming years.

A Validation Approach of an End-to-End Whole Genome Sequencing Workflow for Source Tracking of Listeria monocytogenes and Salmonella enterica

Whole genome sequencing (WGS), using high throughput sequencing technology, reveals the complete sequence of the bacterial genome in a few days. WGS is increasingly being used for source tracking, pathogen surveillance and outbreak investigation due to its high discriminatory power. In the food industry, WGS used for source tracking is beneficial to support contamination investigations. Despite its increased use, no standards or guidelines are available today for the use of WGS in outbreak and/or trace-back investigations. Here we present a validation of our complete (end-to-end) WGS workflow for Listeria monocytogenes and Salmonella enterica including: subculture of isolates, DNA extraction, sequencing and bioinformatics analysis. This end-to-end WGS workflow was evaluated according to the following performance criteria: stability, repeatability, reproducibility, discriminatory power, and epidemiological concordance. The current study showed that few single nucleotide polymorphism (SNPs) were observed for L. monocytogenes and S. enterica when comparing genome sequences from five independent colonies from the first subculture and five independent colonies after the tenth subculture. Consequently, the stability of the WGS workflow for L. monocytogenes and S. enterica was demonstrated despite the few genomic variations that can occur during subculturing steps. Repeatability and reproducibility were also demonstrated. The WGS workflow was shown to have a high discriminatory power and has the ability to show genetic relatedness. Additionally, the WGS workflow was able to reproduce published outbreak investigation results, illustrating its capability of showing epidemiological concordance. The current study proposes a validation approach comprising all steps of a WGS workflow and demonstrates that the workflow can be applied to L. monocytogenes or S. enterica.

Comparison of SNP-based subtyping workflows for bacterial isolates using WGS data, applied to Salmonella enterica serotype Typhimurium and serotype 1,4,[5],12:i:

Whole genome sequencing represents a promising new technology for subtyping of bacterial pathogens. Besides the technological advances which have pushed the approach forward, the last years have been marked by considerable evolution of the whole genome sequencing data analysis methods. Prior to application of the technology as a routine epidemiological typing tool, however, reliable and efficient data analysis strategies need to be identified among the wide variety of the emerged methodologies. In this work, we have compared three existing SNP-based subtyping workflows using a benchmark dataset of 32 Salmonella enterica subsp. enterica serovar Typhimurium and serovar 1,4,[5],12:i:- isolates including five isolates from a confirmed outbreak and three isolates obtained from the same patient at different time points. The analysis was carried out using the original (high-coverage) and a down-sampled (low-coverage) datasets and two different reference genomes. All three tested workflows, namely CSI Phylogeny-based workflow, CFSAN-based workflow and PHEnix-based workflow, were able to correctly group the confirmed outbreak isolates and isolates from the same patient with all combinations of reference genomes and datasets. However, the workflows differed strongly with respect to the SNP distances between isolates and sensitivity towards sequencing coverage, which could be linked to the specific data analysis strategies used therein. To demonstrate the effect of particular data analysis steps, several modifications of the existing workflows were also tested. This allowed us to propose data analysis schemes most suitable for routine SNP-based subtyping applied to S. Typhimurium and S. 1,4,[5],12:i:-. Results presented in this study illustrate the importance of using correct data analysis strategies and to define benchmark and fine-tune parameters applied within routine data analysis pipelines to obtain optimal results.

Phenotypic and Genotypic Eligible Methods for Salmonella Typhimurium Source Tracking

Salmonellosis is one of the most common causes of foodborne infection and a leading cause of human gastroenteritis. Throughout the last decade, Salmonella enterica serotype Typhimurium (ST) has shown an increase report with the simultaneous emergence of multidrug-resistant isolates, as phage type DT104. Therefore, to successfully control this microorganism, it is important to attribute salmonellosis to the exact source. Studies of Salmonella source attribution have been performed to determine the main food/food-production animals involved, toward which, control efforts should be correctly directed. Hence, the election of a ST subtyping method depends on the particular problem that efforts must be directed, the resources and the data available. Generally, before choosing a molecular subtyping, phenotyping approaches such as serotyping, phage typing, and antimicrobial resistance profiling are implemented as a screening of an investigation, and the results are computed using frequency-matching models (i.e., Dutch, Hald and Asymmetric Island models). Actually, due to the advancement of molecular tools as PFGE, MLVA, MLST, CRISPR, and WGS more precise results have been obtained, but even with these technologies, there are still gaps to be elucidated. To address this issue, an important question needs to be answered: what are the currently suitable subtyping methods to source attribute ST. This review presents the most frequently applied subtyping methods used to characterize ST, analyses the major available microbial subtyping attribution models and ponders the use of conventional phenotyping methods, as well as, the most applied genotypic tools in the context of their potential applicability to investigates ST source tracking.

Bacterial genome sequencing in clinical microbiology: a pathogen-oriented review

In recent years, whole-genome sequencing (WGS) has been perceived as a technology with the potential to revolutionise clinical microbiology. Herein, we reviewed the literature on the use of WGS for the most commonly encountered pathogens in clinical microbiology laboratories: Escherichia coli and other Enterobacteriaceae, Staphylococcus aureus and coagulase-negative staphylococci, streptococci and enterococci, mycobacteria and Chlamydia trachomatis. For each pathogen group, we focused on five different aspects: the genome characteristics, the most common genomic approaches and the clinical uses of WGS for (i) typing and outbreak analysis, (ii) virulence investigation and (iii) in silico antimicrobial susceptibility testing. Of all the clinical usages, the most frequent and straightforward usage was to type bacteria and to trace outbreaks back. A next step toward standardisation was made thanks to the development of several new genome-wide multi-locus sequence typing systems based on WGS data. Although virulence characterisation could help in various particular clinical settings, it was done mainly to describe outbreak strains. An increasing number of studies compared genotypic to phenotypic antibiotic susceptibility testing, with mostly promising results. However, routine implementation will preferentially be done in the workflow of particular pathogens, such as mycobacteria, rather than as a broadly applicable generic tool. Overall, concrete uses of WGS in routine clinical microbiology or infection control laboratories were done, but the next big challenges will be the standardisation and validation of the procedures and bioinformatics pipelines in order to reach clinical standards.

High Throughput Sequencing for Detection of Foodborne Pathogens

High-throughput sequencing (HTS) is becoming the state-of-the-art technology for typing of microbial isolates, especially in clinical samples. Yet, its application is still in its infancy for monitoring and outbreak investigations of foods. Here we review the published literature, covering not only bacterial but also viral and Eukaryote food pathogens, to assess the status and potential of HTS implementation to inform stakeholders, improve food safety and reduce outbreak impacts. The developments in sequencing technology and bioinformatics have outpaced the capacity to analyze and interpret the sequence data. The influence of sample processing, nucleic acid extraction and purification, harmonized protocols for generation and interpretation of data, and properly annotated and curated reference databases including non-pathogenic "natural" strains are other major obstacles to the realization of the full potential of HTS in analytical food surveillance, epidemiological and outbreak investigations, and in complementing preventive approaches for the control and management of foodborne pathogens. Despite significant obstacles, the achieved progress in capacity and broadening of the application range over the last decade is impressive and unprecedented, as illustrated with the chosen examples from the literature. Large consortia, often with broad international participation, are making coordinated efforts to cope with many of the mentioned obstacles. Further rapid progress can therefore be prospected for the next decade.

Investigation of Outbreaks of Salmonella enterica Serovar Typhimurium and Its Monophasic Variants Using Whole-Genome Sequencing, Denmark

Whole-genome sequencing is rapidly replacing current molecular typing methods for surveillance purposes. Our study evaluates core-genome single-nucleotide polymorphism analysis for outbreak detection and linking of sources of Salmonella enterica serovar Typhimurium and its monophasic variants during a 7-month surveillance period in Denmark. We reanalyzed and defined 8 previously characterized outbreaks from the phylogenetic relatedness of the isolates, epidemiologic data, and food traceback investigations. All outbreaks were identified, and we were able to exclude unrelated and include additional related human cases. We were furthermore able to link possible food and veterinary sources to the outbreaks. Isolates clustered according to sequence types (STs) 19, 34, and 36. Our study shows that core-genome single-nucleotide polymorphism analysis is suitable for surveillance and outbreak investigation for Salmonella Typhimurium (ST19 and ST36), but whole genome-wide analysis may be required for the tight genetic clone of monophasic variants (ST34).

Investigation of Outbreaks of Salmonella enterica Serovar Typhimurium and Its Monophasic Variants Using Whole-Genome Sequencing, Denmark

Whole-genome sequencing is rapidly replacing current molecular typing methods for surveillance purposes. Our study evaluates core-genome single-nucleotide polymorphism analysis for outbreak detection and linking of sources of Salmonella enterica serovar Typhimurium and its monophasic variants during a 7-month surveillance period in Denmark. We reanalyzed and defined 8 previously characterized outbreaks from the phylogenetic relatedness of the isolates, epidemiologic data, and food traceback investigations. All outbreaks were identified, and we were able to exclude unrelated and include additional related human cases. We were furthermore able to link possible food and veterinary sources to the outbreaks. Isolates clustered according to sequence types (STs) 19, 34, and 36. Our study shows that core-genome single-nucleotide polymorphism analysis is suitable for surveillance and outbreak investigation for Salmonella Typhimurium (ST19 and ST36), but whole genome-wide analysis may be required for the tight genetic clone of monophasic variants (ST34).

Comparative genomics of Australian and international isolates of Salmonella Typhimurium: correlation of core genome evolution with CRISPR and prophage profiles

Salmonella enterica subsp enterica serovar Typhimurium (S. Typhimurium) is a serovar with broad host range. To determine the genomic diversity of S. Typhimurium, we sequenced 39 isolates (37 Australian and 2 UK isolates) representing 14 Repeats Groups (RGs) determined primarily by clustered regularly interspaced short palindromic repeats (CRISPR). Analysis of single nucleotide polymorphisms (SNPs) among the 39 isolates yielded an average of 1,232 SNPs per isolate, ranging from 128 SNPs to 11,339 SNPs relative to the reference strain LT2. Phylogenetic analysis of the 39 isolates together with 66 publicly available genomes divided the 105 isolates into five clades and 19 lineages, with the majority of the isolates belonging to clades I and II. The composition of CRISPR profiles correlated well with the lineages, showing progressive deletion and occasional duplication of spacers. Prophage genes contributed nearly a quarter of the S. Typhimurium accessory genome. Prophage profiles were found to be correlated with lineages and CRISPR profiles. Three new variants of HP2-like P2 prophage, several new variants of P22 prophage and a plasmid-like genomic island StmGI_0323 were found. This study presents evidence of horizontal transfer from other serovars or species and provides a broader understanding of the global genomic diversity of S. Typhimurium.

Multi-provincial Salmonellosis Outbreak Related to Newly Hatched Chicks and Poults: A Genomics Perspective

BACKGROUND

A multi-provincial outbreak of Salmonella enterica serovar Enteritidis was linked to newly hatched chicks and poults from a single hatchery during the spring of 2015. In total, there were 61 human cases that were epidemiologically confirmed to be linked to the chicks and poults and the outbreak was deemed to have ended in the summer of 2015.

METHODS

PulseNet Canada, in coordination with the affected provinces, used genome sequencing of human and agricultural Salmonella Enteritidis isolates to aid in the epidemiological investigation, while also using traditional typing methods such as phagetyping and pulsed-field gel electrophoresis (PFGE).

RESULTS

All human outbreak cases, except one, were Phage Type (PT) 13a. Single nucleotide variant analysis (SNV) was able to provide a level of resolution commensurate with the results of the epidemiological investigation. SNV analysis was also able to separate PT13a outbreak-related isolates from isolates not linked to chicks or poults, while clustering some non-PT13a agricultural strains with the outbreak cluster.

CONCLUSIONS

Based on conventional typing methods (phagetyping or PFGE), clinical and agricultural PT13a SE isolates would have been considered as part of a related cluster. In contrast, phagetyping would have led to the exclusion of several non- PT13a strains that clustered with the outbreak isolates using the genome sequence data. This study demonstrates the improved resolution of genome sequence analysis for coordinated surveillance and source attribution of both human and agricultural SE isolates.

PathoBacTyper: A Web Server for Pathogenic Bacteria Identification and Molecular Genotyping

With the decline in the cost of whole-genome sequencing because of the introduction of next-generation sequencing (NGS) techniques, many public health and clinical laboratories have started to use bacterial whole genomes for epidemiological surveillance and clinical investigation. For epidemiological and clinical purposes in this "NGS era," whole-genome-scale single nucleotide polymorphism (wgSNP) analysis for genotyping is considered suitable. In this paper, we present an online service, PathoBacTyper (http://halst.nhri.org.tw/PathoBacTyper/), for pathogenic bacteria identification and genotyping based on wgSNP analysis. More than 400 pathogenic bacteria can be identified and genotyped through this service. Four data sets containing 59 Salmonella Heidelberg isolates from three outbreaks with the same pulsed-field gel electrophoresis pattern, 34 Salmonella Typhimurium isolates from six outbreaks, 103 isolates of hospital-associated vancomycin-resistant Enterococcus faecium and 15 Legionella pneumophila isolates from clinical and environmental samples in Israel were used for demonstrating the operation and testing the performance of the PathoBacTyper service. The test results reveal the applicability of this service for epidemiological typing and clinical investigation.

Epidemiology and whole genome sequencing of an ongoing point-source Salmonella Agona outbreak associated with sushi consumption in western Sydney, Australia 2015

During May 2015, an increase in Salmonella Agona cases was reported from western Sydney, Australia. We examine the public health actions used to investigate and control this increase. A descriptive case-series investigation was conducted. Six outbreak cases were identified; all had consumed cooked tuna sushi rolls purchased within a western Sydney shopping complex. Onset of illness for outbreak cases occurred between 7 April and 24 May 2015. Salmonella was isolated from food samples collected from the implicated premise and a prohibition order issued. No further cases were identified following this action. Whole genome sequence (WGS) analysis was performed on isolates recovered during this investigation, with additional S. Agona isolates from sporadic-clinical cases and routine food sampling in New South Wales, January to July 2015. Clinical isolates of outbreak cases were indistinguishable from food isolates collected from the implicated sushi outlet. Five additional clinical isolates not originally considered to be linked to the outbreak were genomically similar to outbreak isolates, indicating the point-source contamination may have started before routine surveillance identified an increase. This investigation demonstrated the value of genomics-guided public health action, where near real-time WGS enhanced the resolution of the epidemiological investigation.