MLVA for Salmonella enterica subsp. enterica Serovar Dublin: Development of a Method Suitable for Inter-Laboratory Surveillance and Application in the Context of a Raw Milk Cheese Outbreak in France in 2012

Raw milk cheeses from Beira Baixa, Portugal-A contributive study for the microbiological hygiene and safety assessment

Due to specific bacterial microbiota, raw milk cheeses have appreciated sensory properties. However, they may pose a threat to consumer safety due to potential pathogens presence. This study evaluated the microbiological contamination of 98 raw milk cheeses from Beira Baixa, Portugal. Presence and enumeration of Coagulase Positive Staphylococci (CPS), Listeria monocytogenes, Salmonella spp., pathogenic Escherichia coli, and indicator microorganisms (non-pathogenic E. coli and Listeria spp.) was attained. E. coli antimicrobial resistance (AMR) was also evaluated. PCR and/or Whole genome sequencing (WGS) was used to characterize E. coli, Salmonella spp. and L. monocytogenes isolates. Sixteen cheeses (16.3%) were classified as Satisfactory, 59 (60.2%) as Borderline and 23 (23.5%) as Unsatisfactory/Potential Injurious to Health. L. monocytogenes, CPS > 104 cfu g-1, Extraintestinal pathogenic E. coli (ExPEC) and Salmonella spp. were detected in 4.1%, 6.1%, 3.1% and 1.0% of the samples, respectively. Listeria innocua (4.1%) and E. coli > 104 cfu g-1 (16.3%) were also detected. AMR E. coli was detected in 23/98 (23.5%) of the cheese samples, of which two were multidrug resistant. WGS identified genotypes already associated to human disease and Listeria spp. cluster analysis indicated that cheese contamination might be related with noncompliance with Good Hygiene Practices during cheese production.

Clonal relation between Salmonella enterica subspecies enterica serovar Dublin strains of bovine and food origin in Germany

Salmonella enterica subspecies enterica serovar Dublin (S. Dublin) is a host-adapted serovar causing enteritis and/or systemic diseases in cattle. As the serovar is not host-restricted, it may cause infections in other animals, including humans with severe illness and higher mortality rates than other non-typhoidal serovars. As human infections are mainly caused by contaminated milk, milk products and beef, information on the genetic relationship of S. Dublin strains from cattle and food should be evaluated. Whole-genome sequencing (WGS) of 144 S. Dublin strains from cattle and 30 strains from food origin was performed. Multilocus sequence typing (MLST) revealed mostly sequence type ST-10 from both, cattle and food isolates. In total, 14 of 30 strains from food origin were clonally related to at least one strain from cattle, as detected by core-genome single nucleotide polymorphisms typing as well as core-genome MLST. The remaining 16 foodborne strains fit into the genome structure of S. Dublin in Germany without outliers. WGS proved to be a powerful tool not only to gain information on the epidemiology of Salmonella strains but also to detect clonal relations between organisms isolated from different stages of production. This study has shown a high genetic correlation between S. Dublin strains from cattle and food and, therefore, the potential to cause human infections. S. Dublin strains of both origins share an almost identical set of virulence factors, emphasizing their potential to cause severe clinical manifestations in animals, but also in humans and thus the need for effective control of S. Dublin in a farm-to-fork strategy.

Prevalence, typing and antimicrobial resistance of Salmonella isolates from commercial shellfish in the North coast of Morocco

Salmonellosis is one of the most common foodborne illnesses in the world. The irrational use of antibiotics in medicine and in animal nutrition has greatly favored the emergence and spread of resistant strains of non-typhoid Salmonella. This study aims the determination of the prevalence of Salmonella in bivalve mollusks in Northern Morocco, as well as the molecular typing and antibiotic susceptibility testing of the strains isolated from positive samples. In total, 150 samples from shellfish composed of mussels (Mytilus galloprovincialis), clams (Callista chione and Ruditapes descussatus) and oysters (Magallana gigas). Isolated Salmonella were characterized by Molecular techniques PCR, MLST and MLVA, phylogenetically grouped by MLSA, and susceptibilities were determined for 30 antimicrobial drugs using microdilution method by the BD Phoenix Automated Microbiology System. Prevalence of Salmonella enterica subsp. enterica was 12.67%, grouped in four serovars identified as Chester, Hadar, Typhimurium and Kentucky. Five different MLST STs (sequence types) were detected, ST1954 being the most common, which was mostly found in Chester isolates. Forty-two percent of the isolates showed resistance to more than one antibiotic, especially trimethoprim, sulfa drugs, quinolones and β-lactam. There was a marked change in the serovars and antimicrobial resistance profiles of the Salmonella isolates in this study compared to those in previous studies.

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.

From Cheese-Making to Consumption: Exploring the Microbial Safety of Cheeses through Predictive Microbiology Models

Cheeses are traditional products widely consumed throughout the world that have been frequently implicated in foodborne outbreaks. Predictive microbiology models are relevant tools to estimate microbial behavior in these products. The objective of this study was to conduct a review on the available modeling approaches developed in cheeses, and to identify the main microbial targets of concern and the factors affecting microbial behavior in these products. Listeria monocytogenes has been identified as the main hazard evaluated in modelling studies. The pH, a_w, lactic acid concentration and temperature have been the main factors contemplated as independent variables in models. Other aspects such as the use of raw or pasteurized milk, starter cultures, and factors inherent to the contaminating pathogen have also been evaluated. In general, depending on the production process, storage conditions, and physicochemical characteristics, microorganisms can grow or die-off in cheeses. The classical two-step modeling has been the most common approach performed to develop predictive models. Other modeling approaches, including microbial interaction, growth boundary, response surface methodology, and neural networks, have also been performed. Validated models have been integrated into user-friendly software tools to be used to obtain estimates of microbial behavior in a quick and easy manner. Future studies should investigate the fate of other target bacterial pathogens, such as spore-forming bacteria, and the dynamic character of the production process of cheeses, among other aspects. The information compiled in this study helps to deepen the knowledge on the predictive microbiology field in the context of cheese production and storage.

Genetic diversity and delineation of Salmonella Agona outbreak strains by next generation sequencing, Bavaria, Germany, 1993 to 2018

Background

In 2017, a food-borne Salmonella Agona outbreak caused by infant milk products from a French supplier occurred in Europe. Simultaneously, S. Agona was detected in animal feed samples in Bavaria.

Aim

Using next generation sequencing (NGS) and three data analysis methods, this study’s objectives were to verify clonality of the Bavarian feed strains, rule out their connection to the outbreak, explore the genetic diversity of Bavarian S. Agona isolates from 1993 to 2018 and compare the analysis approaches employed, for practicality and ability to delineate outbreaks caused by the genetically monomorphic Agona serovar.

Methods

In this observational retrospective study, three 2017 Bavarian feed isolates were compared to a French outbreak isolate and 48 S. Agona isolates from our strain collections. The later included human, food, feed, veterinary and environmental isolates, of which 28 were epidemiologically outbreak related. All isolates were subjected to NGS and analysed by: (i) a publicly available species-specific core genome multilocus sequence typing (cgMLST) scheme, (ii) single nucleotide polymorphism phylogeny and (iii) an in-house serovar-specific cgMLST scheme. Using additional international S. Agona outbreak NGS data, the cluster resolution capacity of the two cgMLST schemes was assessed.

Results

We could prove clonality of the feed isolates and exclude their relation to the French outbreak. All approaches confirmed former Bavarian epidemiological clusters.

Conclusion

Even for S. Agona, species-level cgMLST can produce reasonable resolution, being standardisable by public health laboratories. For single samples or homogeneous sample sets, higher resolution by serovar-specific cgMLST or SNP genotyping can facilitate outbreak investigations.

Disentangling a complex nationwide Salmonella Dublin outbreak associated with raw-milk cheese consumption, France, 2015 to 2016

On 18 January 2016, the French National Reference Centre for Salmonella reported to Santé publique France an excess of Salmonella enterica serotype Dublin (S. Dublin) infections. We investigated to identify the source of infection and implement control measures. Whole genome sequencing (WGS) and multilocus variable-number tandem repeat analysis (MLVA) were performed to identify microbiological clusters and links among cases, animal and food sources. Clusters were defined as isolates with less than 15 single nucleotide polymorphisms determined by WGS and/or with identical MLVA pattern. We compared different clusters of cases with other cases (case–case study) and controls recruited from a web-based cohort (case–control study) in terms of food consumption. We interviewed 63/83 (76%) cases; 2,914 controls completed a questionnaire. Both studies’ findings indicated that successive S. Dublin outbreaks from different sources had occurred between November 2015 and March 2016. In the case–control study, cases of distinct WGS clusters were more likely to have consumed Morbier (adjusted odds ratio (aOR): 14; 95% confidence interval (CI): 4.8–42) or Vacherin Mont d’Or (aOR: 27; 95% CI: 6.8–105), two bovine raw-milk cheeses. Based on these results, the Ministry of Agriculture launched a reinforced control plan for processing plants of raw-milk cheeses in the production region, to prevent future outbreaks.

Evaluation of WGS-subtyping methods for epidemiological surveillance of foodborne salmonellosis

BACKGROUND

Salmonellosis is one of the most common foodborne diseases worldwide. Although human infection by non-typhoidal Salmonella (NTS) enterica subspecies enterica is associated primarily with a self-limiting diarrhoeal illness, invasive bacterial infections (such as septicaemia, bacteraemia and meningitis) were also reported. Human outbreaks of NTS were reported in several countries all over the world including developing as well as high-income countries. Conventional laboratory methods such as pulsed field gel electrophoresis (PFGE) do not display adequate discrimination and have their limitations in epidemiological surveillance. It is therefore very crucial to use accurate, reliable and highly discriminative subtyping methods for epidemiological characterisation and outbreak investigation.

METHODS

Here, we used different whole genome sequence (WGS)-based subtyping methods for retrospective investigation of two different outbreaks of Salmonella Typhimurium and Salmonella Dublin that occurred in 2013 in UK and Ireland respectively.

RESULTS

Single nucleotide polymorphism (SNP)-based cluster analysis of Salmonella Typhimurium genomes revealed well supported clades, that were concordant with epidemiologically defined outbreak and confirmed the source of outbreak is due to consumption of contaminated mayonnaise. SNP-analyses of Salmonella Dublin genomes confirmed the outbreak however the source of infection could not be determined. The core genome multilocus sequence typing (cgMLST) was discriminatory and separated the outbreak strains of Salmonella Dublin from the non-outbreak strains that were concordant with the epidemiological data however cgMLST could neither discriminate between the outbreak and non-outbreak strains of Salmonella Typhimurium nor confirm that contaminated mayonnaise is the source of infection, On the other hand, other WGS-based subtyping methods including multilocus sequence typing (MLST), ribosomal MLST (rMLST), whole genome MLST (wgMLST), clustered regularly interspaced short palindromic repeats (CRISPRs), prophage sequence profiling, antibiotic resistance profile and plasmid typing methods were less discriminatory and could not confirm the source of the outbreak.

CONCLUSIONS

Foodborne salmonellosis is an important concern for public health therefore, it is crucial to use accurate, reliable and highly discriminative subtyping methods for epidemiological surveillance and outbreak investigation. In this study, we showed that SNP-based analyses do not only have the ability to confirm the occurrence of the outbreak but also to provide definitive evidence of the source of the outbreak in real-time.

High similarity and high frequency of virulence genes among Salmonella Dublin strains isolated over a 33-year period in Brazil

Salmonella Dublin is a strongly adapted serovar that causes enteritis and/or systemic disease with high rates of mortality in cattle and occasionally infects humans. Despite the importance of this serovar, there is a lack of studies in Brazil. The aim of this study was to characterize the genetic diversity of 112 S. Dublin strains isolated from humans and animals in Brazil by CRISPR and CRISPR-MVLST and the relatedness among strains by MLST. In addition, the frequency of some important virulence genes was verified. The strains studied belonged to nine different sequence types, being all of them single- or double-locus variants of the ST10. CRISPR discriminated the strains into 69 subtypes with a similarity ≥ 84.4% and CRISPR-MVLST into 72 subtypes with a similarity ≥ 84.7%. The virulence genes ratB, lpfA, mgtC, avrA, sopB, sopE2, sifA, sseA, ssrA, csgA, fliC, and sinH were found in all the strains studied, while spvB, spvC, sodCl, rpoS, sipA, sipD, invA, and hilA were detected in ≥ 93.7% of the strains. In conclusion, the high similarity among the strains reinforces the clonal nature of the strains of this serovar that may have descended from a common ancestor that little differed over 33 years in Brazil. CRISPR and CRISPR-MVLST showed to be good alternatives to type S. Dublin strains. MLST suggested that S. Dublin strains from Brazil were phylogenetically related to strains from other parts of the globe. Moreover, the high frequency of virulence genes among the strains studied reinforces the capacity of S. Dublin to cause invasive diseases.

Embracing Diversity: Differences in Virulence Mechanisms, Disease Severity, and Host Adaptations Contribute to the Success of Nontyphoidal Salmonella as a Foodborne Pathogen

Not all Salmonella enterica serovars cause the same disease. S. enterica represents an incredibly diverse species comprising >2,600 unique serovars. While some S. enterica serovars are host-restricted, others infect a wide range of hosts. The diseases that nontyphoidal Salmonella (NTS) serovars cause vary considerably, with some serovars being significantly more likely to cause invasive disease in humans than others. Furthermore, while genomic analyses have advanced our understanding of the genetic diversity of these serovars, they have not been able to fully account for the observed clinical differences. One overarching challenge is that much of what is known about Salmonella's general biology and virulence strategies is concluded from studies examining a select few serovars, especially serovar Typhimurium. As targeted control strategies have been implemented to control select serovars, an increasing number of foodborne outbreaks involving serovars that are less frequently associated with human clinical illness are being detected. Harnessing what is known about the diversity of NTS serovars represents an important factor in achieving the ultimate goal of reducing salmonellosis-associated morbidity and mortality worldwide. In this review we summarize the current understanding of the differences and similarities among NTS serovars, highlighting the virulence mechanisms, genetic differences, and sources that characterize S. enterica diversity and contribute to its success as a foodborne pathogen.

Draft Genome Sequences of Salmonella enterica subsp. enterica Serovar Dublin Strains from St. Nectaire and Morbier Cheeses Characterized by Multilocus Variable-Number Tandem-Repeat Analysis Profiles Associated with Two Fatal Outbreaks in France

We report here the draft genome sequences of 2 Salmonella enterica subsp. enterica serovar Dublin strains from St. Nectaire and Morbier cheeses having multilocus variable-number tandem-repeat analysis (MLVA) profiles identified during the fatal outbreaks that occurred in France in 2012 and 2015 to 2016, respectively.

We report here the draft genome sequences of 2 Salmonella enterica subsp. enterica serovar Dublin strains from St. Nectaire and Morbier cheeses having multilocus variable-number tandem-repeat analysis (MLVA) profiles identified during the fatal outbreaks that occurred in France in 2012 and 2015 to 2016, respectively. These draft genome sequences will help uncover the virulence determinants in invasive S. Dublin strains.

Evaluation of whole-genome sequencing for outbreak detection of Verotoxigenic Escherichia coli O157:H7 from the Canadian perspective

BACKGROUND

Rapid and accurate identification of Verotoxigenic Escherichia coli (VTEC) O157:H7 is dependent on well-established, standardized and highly discriminatory typing methods. Currently, conventional subtyping tests for foodborne bacterial pathogen surveillance are rapidly being replaced with whole-genome sequencing (WGS) in public health laboratories. The capacity of WGS to revolutionize global foodborne disease surveillance has positioned this tool to become the new gold standard; however, to ensure evidence standards for public health decision making can still be achieved, the performance of WGS must be thoroughly validated against current gold standard methods prior to implementation. Here we aim to verify the performance of WGS in comparison to pulsed-field gel electrophoresis (PFGE) and multiple-locus variable-number tandem repeat analysis (MLVA) for eight retrospective outbreaks of VTEC O157:H7 from the Canadian perspective. Since real-time implementation and routine use of WGS in public health laboratories is highly reliant on standardized data analysis tools, we also provide a comparative analysis of two popular methodologies for WGS analyses; an in-house developed single nucleotide variant phylogenomics (SNVPhyl) pipeline and the BioNumerics whole genome multilocus sequence typing (wgMLST) tool. To provide a useful and consistent starting point for examining laboratory-based surveillance data for VTEC O157:H7 in Canada, we also aim to describe the number of genetic differences observed among outbreak-associated isolates.

RESULTS

WGS provided enhanced resolution over traditional subtyping methods, and accurately distinguished outbreak-related isolates from non-outbreak related isolates with high epidemiological concordance. WGS also illuminated potential linkages between sporadic cases of illness and contaminated food, and isolates spanning multiple years. The topologies generated by SNVPhyl and wgMLST were highly congruent with strong statistical support. Few genetic differences were observed among outbreak-related isolates (≤5 SNVs/ < 10 wgMLST alleles) unless the outbreak was suspected to be multi-strain.

CONCLUSIONS

This study validates the superiority of WGS and indicates the BioNumerics wgMLST schema is suitable for surveillance and cluster detection of VTEC O157:H7. These findings will provide a useful and consistent starting point for examining WGS data for prospective laboratory-based surveillance of VTEC O157:H7, but however, the data will continue to be interpreted according to context and in combination with epidemiological and food safety evidence to inform public-health decision making in Canada.

Molecular-based identification and detection of Salmonella in food production systems: current perspectives

Salmonella remains a prominent cause of foodborne illnesses and can originate from a wide range of food products. Given the continued presence of pathogenic Salmonella in food production systems, there is a consistent need to improve identification and detection methods that can identify this pathogen at all stages in food systems. Methods for subtyping have evolved over the years, and the introduction of whole genome sequencing and advancements in PCR technologies have greatly improved the resolution for differentiating strains within a particular serovar. This, in turn, has led to the continued improvement in Salmonella detection technologies for utilization in food production systems. In this review, the focus will be on recent advancements in these technologies, as well as potential issues associated with the application of these tools in food production. In addition, the recent and emerging research developments on Salmonella detection and identification methodologies and their potential application in food production systems will be discussed.