Whole genome sequencing of Salmonella Typhimurium illuminates distinct outbreaks caused by an endemic multi-locus variable number tandem repeat analysis type in Australia, 2014

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.

Validating the Utility of Multilocus Variable Number Tandem-repeat Analysis (MLVA) as a Subtyping Strategy to Monitor Listeria monocytogenes In-built Food Processing Environments

Listeria monocytogenes is a serious human pathogen and an enduring challenge to control for the ready-to-eat food processing industry. Cost-effective tools that can be deployed by commercial or in-house laboratories to rapidly investigate and resolve contamination events in the built food processing environment are of value to the food industry. Multilocus variable number tandem-repeat analysis (MLVA) is a molecular subtyping method, which along with other same-generation methods such as pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST) is being superseded in disease tracking and outbreak investigations by whole-genome sequencing (WGS). In this paper, it is demonstrated that MLVA can continue to play a valuable role as a valid, fast, simple, and cost-effective method to identify and track Listeria monocytogenes subtypes in factory environments, with the method being highly congruent with MLST. Although MLVA does not have the discriminatory power of WGS to identify truly persistent clones, with careful interpretation of results alongside isolate metadata, it remains a powerful tool in situations and locations where WGS may not be readily available to food business operators.

Evidence of international transmission of mobile colistin resistant monophasic Salmonella Typhimurium ST34

S. 4,[5],12:i:-, a monophasic variant of S. enterica serovar Typhimurium, is an important multidrug resistant serovar. Strains of colistin-resistant S. 4,[5],12:i:- have been reported in several countries with patients occasionally had recent histories of travels to Southeast Asia. In the study herein, we investigated the genomes of S. 4,[5],12:i:- carrying mobile colistin resistance (mcr) gene in Thailand. Three isolates of mcr-3.1 carrying S. 4,[5],12:i:- in Thailand were sequenced by both Illumina and Oxford Nanopore platforms and we analyzed the sequences together with the whole genome sequences of other mcr-3 carrying S. 4,[5],12:i:- isolates available in the NCBI Pathogen Detection database. Three hundred sixty-nine core genome SNVs were identified from 27 isolates, compared to the S. Typhimurium LT2 reference genome. A maximum-likelihood phylogenetic tree was constructed and revealed that the samples could be divided into three clades, which correlated with the profiles of fljAB-hin deletions and plasmids. A couple of isolates from Denmark had the genetic profiles similar to Thai isolates, and were from the patients who had traveled to Thailand. Complete genome assembly of the three isolates revealed the insertion of a copy of IS26 at the same site near iroB, suggesting that the insertion was an initial step for the deletions of fljAB-hin regions, the hallmark of the 4,[5],12:i:- serovar. Six types of plasmid replicons were identified with the majority being IncA/C. The coexistence of mcr-3.1 and bla_CTX-M-55 was found in both hybrid-assembled IncA/C plasmids but not in IncHI2 plasmid. This study revealed possible transmission links between colistin resistant S. 4,[5],12:i:- isolates found in Thailand and Denmark and confirmed the important role of plasmids in transferring multidrug resistance.

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.

Optimization and Validation of Candida auris Short Tandem Repeat Analysis

Candida auris is an easily transmissible yeast with resistance to different antifungal compounds. Outbreaks of C. auris are mostly observed in intensive care units. To take adequate measures during an outbreak, it is essential to understand the transmission route, which requires isolate genotyping. In 2019, a short tandem repeat (STR) genotyping analysis was developed for C. auris. To determine the discriminatory power of this method, we performed STR analysis of 171 isolates with known whole-genome sequencing (WGS) data using Illumina reads, and we compared their resolutions. We found that STR analysis separated the 171 isolates into four clades (clades I to IV), as was also seen with WGS analysis. Then, to improve the separation of isolates in clade IV, the STR assay was optimized by the addition of 2 STR markers. With this improved STR assay, a total of 32 different genotypes were identified, while all isolates with differences of >50 single-nucleotide polymorphisms (SNPs) were separated by at least 1 STR marker. Altogether, we optimized and validated the C. auris STR panel for clades I to IV and established its discriminatory power, compared to WGS SNP analysis using Illumina reads. IMPORTANCE The emerging fungal pathogen Candida auris poses a threat to public health, mainly causing outbreaks in intensive care units. Genotyping is essential for investigating potential outbreaks and preventing further spread. Previously, we developed a STR genotyping scheme for rapid and high-resolution genotyping, and WGS SNP outcomes for some isolates were compared to STR data. Here, we compared WGS SNP and STR outcomes for a larger sample cohort. Also, we optimized the resolution of this typing scheme with the addition of 2 STR markers. Altogether, we validated and optimized this rapid, reliable, and high-resolution typing scheme for C. auris.

Exploring the predictive capability of advanced machine learning in identifying severe disease phenotype in Salmonella enterica

The past few years have seen a significant increase in availability of whole genome sequencing information, allowing for its incorporation in predictive modeling for foodborne pathogens to account for inter- and intra-species differences in their virulence. However, this is hindered by the inability of traditional statistical methods to analyze such large amounts of data compared to the number of observations/isolates. In this study, we have explored the applicability of machine learning (ML) models to predict the disease outcome, while identifying features that exert a significant effect on the prediction. This study was conducted on Salmonella enterica, a major foodborne pathogen with considerable inter- and intra-serovar variation. WGS of isolates obtained from various sources (i.e., human, chicken, and swine) were used as input in four machine learning models (logistic regression with ridge, random forest, support vector machine, and AdaBoost) to classify isolates based on disease severity (extraintestinal vs. gastrointestinal) in the host. The predictive performances of all models were tested with and without Elastic Net regularization to combat dimensionality issues. Elastic Net-regularized logistic regression model showed the best area under the receiver operating characteristic curve (AUC-ROC; 0.86) and outcome prediction accuracy (0.76). Additionally, genes coding for transcriptional regulation, acidic, oxidative, and anaerobic stress response, and antibiotic resistance were found to be significant predictors of disease severity. These genes, which were significantly associated with each outcome, could possibly be input in amended, gene-expression-specific predictive models to estimate virulence pattern-specific effect of Salmonella and other foodborne pathogens on human health.

Evaluation of Antibiotic Resistance of Salmonella Serotypes and Whole-Genome Sequencing of Multiresistant Strains Isolated from Food Products in Russia

Food products may be a source of Salmonella, one of the main causal agents of food poisoning, especially after the emergence of strains resistant to antimicrobial preparations. The present work dealt with investigation of the occurrence of resistance to antimicrobial preparations among S. enterica strains isolated from food. The isolates belonged to 11 serovars, among which Infantis (28%), Enteritidis (19%), and Typhimurium (13.4%) predominated. The isolates were most commonly resistant to trimethoprim/sulfamethoxazole (n = 19, 59.38%), cefazolin (n = 15, 46.86%), tetracycline (n = 13, 40.63%), and amikacin (n = 9, 28.13%). Most of the strains (68.75%) exhibited multiple resistance to commonly used antibiotics. High-throughput sequencing was used to analyse three multidrug-resistant strains (resistant to six or more antibiotics). Two of them (SZL 30 and SZL 31) belonged to S. Infantis, while one strain belonged to S. Typhimurium (SZL 38). Analysis of the genomes of the sequenced strains revealed the genes responsible for antibiotic resistance. In the genomes of strains SZL 30 and SZL 31 the genes of antibiotic resistance were shown to be localized mostly in integrons within plasmids, while most of the antibiotic resistance genes of strain SZL 38 were localized in a chromosomal island (17,949 nt). Genomes of the Salmonella strains SZL 30, SZL 31, and SZL 38 were shown to contain full-size pathogenicity islands: SPI-1, SPI-2, SPI-4, SPI-5, SPI-9, SPI-11, SPI-13, SPI-14, and CS54. Moreover, the genome of strain SZL 38 was also found to contain the full-size pathogenicity islands SPI-3, SPI-6, SPI-12, and SPI-16. The emergence of multidrug-resistant strains of various Salmonella serovars indicates that further research on the transmission pathways for these genetic determinants and monitoring of the distribution of these microorganisms are necessary.

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.

Occurrence, antimicrobial resistance and whole genome sequence analysis of Salmonella serovars from pig farms in Ilorin, North-central Nigeria

Salmonella enterica is a foodborne pathogen of global public health importance with developing countries mostly affected. Foodborne outbreaks are often attributed to pork consumption and Salmonella contamination of retail pork is directly linked to the Salmonella prevalence on farm. The widespread use of antimicrobials at different steps of swine production can favor resistant strains of Salmonella. The objectives of this study are to characterize the distribution, multilocus sequence typing (MLST), plasmid, virulence profiles and antimicrobial resistance of Salmonella serovars circulating in selected pig farms. Six hundred fecal samples were randomly collected from nine selected farms in Ilorin, Nigeria. Isolates were analyzed by cultural isolation using selective media, conventional biochemical characterization, serotyping, MLST and whole genome sequencing (WGS). Sixteen samples were positive for Salmonella sub-species, comprising of nine serovars. The antimicrobial susceptibility results revealed low-level resistance against 13 antimicrobial agents. Five strains exhibited resistance to nalidixic acid and intermediate resistance to ciprofloxacin with chromosomal (double) mutation at gyrA and parC while four strains possessed single mutation in parC. Salmonella Kentucky showed double mutation each at gyrA and parC. WGS analysis, revealed eight diverse sequence types (STs), the most common STs were ST-321 and ST-19 (n = 4) exhibited by S. Muenster and S. Typhimurium, respectively. Single Nucleotide Polymorphism (SNP)-based phylogeny analysis showed the 16 isolates to be highly related and fell into 8 existing clusters at NCBI Pathogen Detection. Curtailing the spread of resistant strains will require the establishment of continuous surveillance program at the state and national levels in Nigeria. This study provides useful information for further studies on antimicrobial resistance mechanisms in foodborne Salmonella species.

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.

Toward an Integrated Genome-Based Surveillance of Salmonella enterica in Germany

Despite extensive monitoring programs and preventative measures, Salmonella spp. continue to cause tens of thousands human infections per year, as well as many regional and international food-borne outbreaks, that are of great importance for public health and cause significant socio-economic costs. In Germany, salmonellosis is the second most common cause of bacterial diarrhea in humans and is associated with high hospitalization rates. Whole-genome sequencing (WGS) combined with data analysis is a high throughput technology with an unprecedented discriminatory power, which is particularly well suited for targeted pathogen monitoring, rapid cluster detection and assignment of possible infection sources. However, an effective implementation of WGS methods for large-scale microbial pathogen detection and surveillance has been hampered by the lack of standardized methods, uniform quality criteria and strategies for data sharing, all of which are essential for a successful interpretation of sequencing data from different sources. To overcome these challenges, the national GenoSalmSurv project aims to establish a working model for an integrated genome-based surveillance system of Salmonella spp. in Germany, based on a decentralized data analysis. Backbone of the model is the harmonization of laboratory procedures and sequencing protocols, the implementation of open-source bioinformatics tools for data analysis at each institution and the establishment of routine practices for cross-sectoral data sharing for a uniform result interpretation. With this model, we present a working solution for cross-sector interpretation of sequencing data from different sources (such as human, veterinarian, food, feed and environmental) and outline how a decentralized data analysis can contribute to a uniform cluster detection and facilitate outbreak investigations.

Nonsynonymous Polymorphism Counts in Bacterial Genomes: a Comparative Examination

Genomic data reveal single-nucleotide polymorphisms (SNPs) that may carry information about the evolutionary history of bacteria. However, it remains unclear what inferences about selection can be made from genomic SNP data. Bacterial species are often sampled during epidemic outbreaks or within hosts during the course of chronic infections. SNPs obtained from genomic analysis of these data are not necessarily fixed. Treating them as fixed during analysis by using measures such as the ratio of nonsynonymous to synonymous evolutionary changes (dN/dS) may lead to incorrect inferences about the strength and direction of selection. In this study, we consider data from a range of whole-genome sequencing studies of bacterial pathogens and explore patterns of nonsynonymous variation to assess whether evidence of selection can be identified by investigating SNP counts alone across multiple WGS studies. We visualize these SNP data in ways that highlight their relationship to neutral baseline expectations. These neutral expectations are based on a simple model of mutation, from which we simulate SNP accumulation to investigate how SNP counts are distributed under alternative assumptions about positive and negative selection. We compare these patterns with empirical SNP data and illustrate the general difficulty of detecting positive selection from SNP data. Finally, we consider whether SNP counts observed at the between-host population level differ from those observed at the within-host level and find some evidence that suggests that dynamics across these two scales are driven by different underlying processes.IMPORTANCE Identifying selection from SNP data obtained from whole-genome sequencing studies is challenging. Some current measures used to identify and quantify selection acting on genomes rely on fixed differences; thus, these are inappropriate for SNP data where variants are not fixed. With the increase in whole-genome sequencing studies, it is important to consider SNP data in the context of evolutionary processes. How SNPs are counted and analyzed can help in understanding mutation accumulation and trajectories of strains. We developed a tool for identifying possible evidence of selection and for comparative analysis with other SNP data. We propose a model that provides a rule-of-thumb guideline and two new visualization techniques that can be used to interpret and compare SNP data. We quantify the expected proportion of nonsynonymous SNPs in coding regions under neutrality and demonstrate its use in identifying evidence of positive and negative selection from simulations and empirical data.

Combining Whole-Genome Sequencing and Multimodel Phenotyping To Identify Genetic Predictors of Salmonella Virulence

Salmonella comprises more than 2,600 serovars. Very few environmental and uncommon serovars have been characterized for their potential role in virulence and human infections. A complementary in vitro and in vivo systematic high-throughput analysis of virulence was used to elucidate the association between genetic and phenotypic variations across Salmonella isolates. The goal was to develop a strategy for the classification of isolates as a benchmark and predict virulence levels of isolates. Thirty-five phylogenetically distant strains of unknown virulence were selected from the Salmonella Foodborne Syst-OMICS (SalFoS) collection, representing 34 different serovars isolated from various sources. Isolates were evaluated for virulence in 4 complementary models of infection to compare virulence traits with the genomics data, including interactions with human intestinal epithelial cells, human macrophages, and amoeba. In vivo testing was conducted using the mouse model of Salmonella systemic infection. Significant correlations were identified between the different models. We identified a collection of novel hypothetical and conserved proteins associated with isolates that generate a high burden. We also showed that blind prediction of virulence of 33 additional strains based on the pan-genome was high in the mouse model of systemic infection (82% agreement) and in the human epithelial cell model (74% agreement). These complementary approaches enabled us to define virulence potential in different isolates and present a novel strategy for risk assessment of specific strains and for better monitoring and source tracking during outbreaks.IMPORTANCE Salmonella species are bacteria that are a major source of foodborne disease through contamination of a diversity of foods, including meat, eggs, fruits, nuts, and vegetables. More than 2,600 different Salmonella enterica serovars have been identified, and only a few of them are associated with illness in humans. Despite the fact that they are genetically closely related, there is enormous variation in the virulence of different isolates of Salmonella enterica Identification of foodborne pathogens is a lengthy process based on microbiological, biochemical, and immunological methods. Here, we worked toward new ways of integrating whole-genome sequencing (WGS) approaches into food safety practices. We used WGS to build associations between virulence and genetic diversity within 83 Salmonella isolates representing 77 different Salmonella serovars. Our work demonstrates the potential of combining a genomics approach and virulence tests to improve the diagnostics and assess risk of human illness associated with specific Salmonella isolates.

A One Health investigation of Salmonella enterica serovar Wangata in north-eastern New South Wales, Australia, 2016-2017

Salmonella enterica serovar Wangata (S. Wangata) is an important cause of endemic salmonellosis in Australia, with human infections occurring from undefined sources. This investigation sought to examine possible environmental and zoonotic sources for human infections with S. Wangata in north-eastern New South Wales (NSW), Australia. The investigation adopted a One Health approach and was comprised of three complimentary components: a case–control study examining human risk factors; environmental and animal sampling; and genomic analysis of human, animal and environmental isolates. Forty-eight human S. Wangata cases were interviewed during a 6-month period from November 2016 to April 2017, together with 55 Salmonella Typhimurium (S. Typhimurium) controls and 130 neighbourhood controls. Indirect contact with bats/flying foxes (S. Typhimurium controls (adjusted odds ratio (aOR) 2.63, 95% confidence interval (CI) 1.06–6.48)) (neighbourhood controls (aOR 8.33, 95% CI 2.58–26.83)), wild frogs (aOR 3.65, 95% CI 1.32–10.07) and wild birds (aOR 6.93, 95% CI 2.29–21.00) were statistically associated with illness in multivariable analyses. S. Wangata was detected in dog faeces, wildlife scats and a compost specimen collected from the outdoor environments of cases’ residences. In addition, S. Wangata was detected in the faeces of wild birds and sea turtles in the investigation area. Genomic analysis revealed that S. Wangata isolates were relatively clonal. Our findings suggest that S. Wangata is present in the environment and may have a reservoir in wildlife populations in north-eastern NSW. Further investigation is required to better understand the occurrence of Salmonella in wildlife groups and to identify possible transmission pathways for human infections.

Phylogenetic analysis revealed that Salmonella Typhimurium ST313 isolated from humans and food in Brazil presented a high genomic similarity

Salmonella Typhimurium sequence type 313 (S. Typhimurium ST313) has caused invasive disease mainly in sub-Saharan Africa. In Brazil, ST313 strains have been recently described, and there is a lack of studies that assessed by whole genome sequencing (WGS)-the relationship of these strains. The aims of this work were to study the phylogenetic relationship of 70 S. Typhimurium genomes comparing strains of ST313 (n = 9) isolated from humans and food in Brazil among themselves, with other STs isolated in this country (n = 31) and in other parts of the globe (n = 30) by 16S rRNA sequences, the Gegenees software, whole genome multilocus sequence typing (wgMLST), and average nucleotide identity (ANI) for the genomes of ST313. Additionally, pangenome analysis was performed to verify the heterogeneity of these genomes. The phylogenetic analyses showed that the ST313 genomes were very similar among themselves. However, the ST313 genomes were usually clustered more distantly to other STs of strains isolated in Brazil and in other parts of the world. By pangenome calculation, the core genome was 2,880 CDSs and 4,171 CDSs singletons for all the 70 S. Typhimurium genomes studied. Considering the 10 ST313 genomes analyzed the core genome was 4,112 CDSs and 76 CDSs singletons. In conclusion, the ST313 genomes from Brazil showed a high similarity among them which information might eventually help in the development of vaccines and antibiotics. The pangenome analysis showed that the S. Typhimurium genomes studied presented an open pangenome, but specifically tending to become close for the ST313 strains.

Childhood Diarrhoea in the Eastern Mediterranean Region with Special Emphasis on Non-Typhoidal Salmonella at the Human⁻Food Interface

Diarrhoeal disease is still one of the most challenging issues for health in many countries across the Eastern Mediterranean region (EMR), with infectious diarrhoea being an important cause of morbidity and mortality, especially in children under five years of age. However, the understanding of the aetiological spectrum and the burden of enteric pathogens involved in diarrhoeal disease in the EMR is incomplete. Non-typhoidal Salmonella (NTS), the focus of this review, is one of the most frequently reported bacterial aetiologies in diarrhoeal disease in the EMR. Strains of NTS with resistance to antimicrobial drugs are increasingly reported in both developed and developing countries. In the EMR, it is now widely accepted that many such resistant strains are zoonotic in origin and acquire their resistance in the food-animal host before onward transmission to humans through the food chain. Here, we review epidemiological and microbiological aspects of diarrhoeal diseases among children in the EMR, with emphasis on the implication and burden of NTS. We collate evidence from studies across the EMR on the zoonotic exposure and antimicrobial resistance in NTS at the interface between human and foods of animal origin. This review adds to our understanding of the global epidemiology of Salmonella with emphasis on the current situation in the EMR.

Clinically Unreported Salmonellosis Outbreak Detected via Comparative Genomic Analysis of Municipal Wastewater Salmonella Isolates

Municipal wastewater includes human waste that contains both commensal and pathogenic enteric microorganisms, and this collective community microbiome can be monitored for community diseases. In a previous study, we assessed the salmonellosis disease burden using municipal wastewater from Honolulu, Hawaii, which was monitored over a 54-week period. During that time, a strain of Salmonella enterica serovar Paratyphi B variant L(+) tartrate(+) (also known as Salmonella enterica serovar Paratyphi B variant Java) was identified; this strain was detected simultaneously with a clinically reported outbreak, and pulsed-field gel electrophoresis patterns were identical for clinical and municipal wastewater isolates. Months after the outbreak subsided, the same pulsotype was detected as the dominant pulsotype in municipal wastewater samples, with no corresponding clinical cases reported. Using genomic characterization (including core single-nucleotide polymorphism alignment, core genome multilocus sequence typing, and screening for virulence and antibiotic resistance genes), all S Java municipal wastewater isolates were determined to be clonal, indicating a resurgence of the original outbreak strain. This demonstrates the feasibility and utility of municipal wastewater surveillance for determining enteric disease outbreaks that may be missed by traditional clinical surveillance methods.IMPORTANCE Underdetection of microbial infectious disease outbreaks in human communities carries enormous health costs and is an ongoing problem in public health monitoring (which relies almost exclusively on data from health clinics). Surveillance of municipal wastewater for community-level monitoring of infectious disease burdens has the potential to fill this information gap, due to its easy access to the mixed community microbiome. In the present study, the genomes of 21 S Java isolates (collected from municipal wastewater in Honolulu) were analyzed; results showed that the same Salmonella strain that caused a known salmonellosis clinical outbreak in spring 2010 remerged as the most dominant strain in municipal wastewater in spring 2011, indicating a new outbreak that was not detected by health clinics. Our results show that wastewater monitoring holds great promise to inform the field of public health regarding outbreak status within communities.

Diversity of Salmonella serotypes from humans, food, domestic animals and wildlife in New South Wales, Australia

BACKGROUND

Salmonella is an important human pathogen in Australia and annual case rates continue to increase. In addition to foodborne exposures, cases have been associated with animal and contaminated environment contact. However, routine surveillance in Australia has tended to focus on humans and food, with no reported attempts to collate and compare Salmonella data from a wider range of potential sources of exposure.

METHODS

Salmonella data from humans, food, animals and environments were collated from a range of surveillance and diagnostic sources in New South Wales (NSW). Data were categorised to reflect one of 29 sample origins. Serotype diversity was described for each category, and the distribution of serotypes commonly isolated from humans was examined for each sample origin. The distribution of serotypes along the livestock-food-human continuum and at the companion animal-wildlife interface was also examined.

RESULTS

In total, 49,872 Salmonella isolates were included in this analysis, comprising 325 serotypes. The vast majority of these isolates were from humans (n = 38,106). Overall S. Typhimurium was the most frequently isolated serotype and was isolated from all sample categories except natural environment and game meat. S. Enteriditis was not isolated from any livestock animal, however sporadic cases were documented in food, companion animals and a reptile. Many serotypes that were frequently isolated from livestock animals and associated food products were only rarely isolated from humans. In addition, a number of key human serotypes were only sporadically isolated from livestock and food products, suggesting alternative sources of infection. In particular, S. Paratyphi B Java and S. Wangata were more often isolated from wild animals. Finally, there was some overlap between serotypes in companion animals and wildlife, with cats in particular having a large number of serotypes in common with wild birds.

CONCLUSIONS

This is the most comprehensive description of Salmonella data from humans, food, livestock, wildlife, companion animals and various environments in Australia reported to date. Results confirm that livestock and food are important sources of salmonellosis in humans but that alternative sources - such as contact with wildlife and environments - warrant further investigation. Surveillance in NSW is largely human-focussed: major knowledge gaps exist regarding the diversity and frequency of serotypes in animals. More systematic surveillance of domestic animals and wildlife is needed to inform targeted control strategies and quantitative source attribution modelling in this state.

Optimization of subculture and DNA extraction steps within the whole genome sequencing workflow for source tracking of Salmonella enterica and Listeria monocytogenes

This work shows that an incubation time reduced to 4-5 h to prepare a culture for DNA extraction followed by an automated DNA extraction can shorten the hands-on time, the turnaround time by 30% and increase the throughput while maintaining the WGS quality assessed by high quality Single Nucleotide Polymorphism analysis.

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.

Retrospective genome-wide comparisons of Salmonella enterica serovar Enteritidis from suspected outbreaks in Singapore

The number of salmonellosis cases in Singapore has increased over the years. Salmonella enterica serovar Enteritidis has always been the most predominant serovar in the last five years. The National Public Health Laboratory assisted outbreak investigations by performing multilocus variable number tandem repeat analysis (MLVA) on isolates that were collected at the time of the investigations. Isolates were defined as belonging to a particular cluster if they had identical MLVA patterns. Whilst MLVA has been instrumental in outbreak investigations, it may not be useful when outbreaks are caused by an endemic MLVA type. In this study, we analysed 67 isolates from 12 suspected outbreaks with known epidemiological links to explore the use of next-generation sequencing (NGS) for defining outbreaks. We found that NGS can confidently group isolates into their respective outbreaks. The isolates from each suspected outbreak were closely related and differed by a maximum of 3 single nucleotide polymorphisms (SNPs). They were also clearly separated from isolates that belonged to different suspected outbreaks. This study provides an important insight and further evidence on the value of NGS for routine surveillance and outbreak detection of S. Enteritidis.

Use of multiple-locus variable-number of tandem repeats analysis (MLVA) to investigate genetic diversity of Salmonella enterica subsp. enterica serovar Typhimurium isolates from human, food, and veterinary sources

Salmonella enterica subspecies enterica serovar Typhimurium is the most common zoonotic pathogen in Bulgaria. To allow efficient outbreak investigations and surveillance in the food chain, accurate and discriminatory methods for typing are needed. This study evaluated the use of multiple-locus variable-number of tandem repeats analysis (MLVA) and compared results with antimicrobial resistance (AMR) determinations for 100 S. Typhimurium strains isolated in Bulgaria during 2008-2012 (50 veterinary/food and 50 human isolates). Results showed that isolates were divided into 80 and 34 groups using MLVA and AMR, respectively. Simpson's index of diversity was determined to 0.994 ± 0.003 and 0.945 ± 0.012. The most frequently encountered MLVA profiles were 3-11-9-NA-211 (n = 5); 3-12-9-NA-211 (n = 3); 3-12-11-21-311 (n = 3); 3-17-10-NA-311 (n = 3); 2-20-9-7-212 (n = 3); and 2-23-NA-NA-111 (n = 3). No clustering of isolates related to susceptibility/resistance to antimicrobials, source of isolation, or year of isolation was observed. Some MLVA types were found in both human and veterinary/food isolates, indicating a possible route of transmission. A majority (83%) of the isolates were found to be resistant against at least one antimicrobial and 44% against ≥4 antimicrobials. Further studies are needed to verify MLVA usefulness over a longer period of time and with more isolates, including outbreak strains.

Diagnostics and laboratory role in outbreaks

PURPOSE OF REVIEW

The review describes the investigative benefits of traditional and novel molecular epidemiology techniques, while acknowledging the limitations faced by clinical laboratories seeking to implement these methods.

RECENT FINDINGS

Pulse-field gel electrophoresis and other traditional techniques remain powerful tools in outbreak investigations and continue to be used by multiple groups. Newer techniques such as matrix-assisted laser desorption/ionization-time of flight mass-spectrometry and whole genome sequencing show great promise. However, there is a lack of standardization regarding definitions for genetic relatedness, nor are there established criteria for accuracy and reproducibility. There are also challenges regarding availability of trained bioinformatics staff, and concerns regarding reimbursement.

SUMMARY

There are many tools available for molecular epidemiologic investigation. Epidemiologists and clinical laboratorians should work together to determine which testing methods are best for each institution.

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.

The movement of humans and the spread of Salmonella into existing and pristine ecosystems

The spread of infectious diseases by the international and national movement of people, animals, insects and products has a documented history dating back several centuries1. The role of human movements has been fundamental to this, and has increased as global travel has risen in amount and speed. This has been exemplified by international epidemics of influenza, antimicrobial resistant bacteria, SARS coronavirus, dengue, chikungunya virus, Zika viruses and many others. Foodborne pathogens have also regularly come to our attention for their ability to move internationally, and outbreaks of salmonellosis due to importation of contaminated foods are well described2,3. An extensive collection of non-typhoidal Salmonella and related species isolated from human, food, animal and environmental sources has been accumulated within Western Australia (WA) since the mid-20th century, and has proven an important historical source of information about the role of humans in the dissemination of microorganisms across and within diverse ecosystems4–6. It is clear that the movement of microorganisms into and out of Australia is by no means a new phenomenon, and that humans have been important contributors to that spread. These are important markers of our impact on established and pristine ecosystems.