Dataset Information

Whole-Genome Sequencing and Bioinformatic Analysis of Isolates from Foodborne Illness Outbreaks of Campylobacter jejuni and Salmonella enterica.

ABSTRACT: Whole-genome sequencing (WGS) via next-generation sequencing (NGS) technologies is a powerful tool for determining the relatedness of bacterial isolates in foodborne illness detection and outbreak investigations. WGS has been applied to national outbreaks (for example, Listeria monocytogenes); however, WGS has rarely been used in smaller local outbreaks. The current study demonstrates the superior resolution of genetic and evolutionary relatedness generated by WGS data analysis, compared to pulsed-field gel electrophoresis (PFGE). The current study retrospectively applies WGS and a reference-free bioinformatic analysis to a Utah-specific outbreak of Campylobacter jejuni associated with raw milk and to a national multistate outbreak of Salmonella enterica subsp. enterica serovar Typhimurium associated with rotisserie chicken, both of which were characterized previously by PFGE. Together, these analyses demonstrate how a reference-free WGS workflow is not reliant on determination of a reference sequence, like WGS workflows that are based on single-nucleotide polymorphisms, or the need for curated allele databases, like multilocus sequence typing workflows.

SUBMITTER: Oakeson KF

PROVIDER: S-EPMC6204689 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Whole-Genome Sequencing and Bioinformatic Analysis of Isolates from Foodborne Illness Outbreaks of Campylobacter jejuni and Salmonella enterica.

Oakeson Kelly F KF Wagner Jennifer Marie JM Rohrwasser Andreas A Atkinson-Dunn Robyn R

Journal of clinical microbiology 20181025 11

Whole-genome sequencing (WGS) via next-generation sequencing (NGS) technologies is a powerful tool for determining the relatedness of bacterial isolates in foodborne illness detection and outbreak investigations. WGS has been applied to national outbreaks (for example, <i>Listeria monocytogenes</i>); however, WGS has rarely been used in smaller local outbreaks. The current study demonstrates the superior resolution of genetic and evolutionary relatedness generated by WGS data analysis, compared ...[more]

PMID: 30158193

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Project description:Establishing an association between possible food sources and clinical isolates requires discriminating the suspected pathogen from an environmental background, and distinguishing it from other closely-related foodborne pathogens. We used whole genome sequencing (WGS) to Salmonella subspecies enterica serotype Tennessee (S. Tennessee) to describe genomic diversity across the serovar as well as among and within outbreak clades of strains associated with contaminated peanut butter. We analyzed 71 isolates of S. Tennessee from disparate food, environmental, and clinical sources and 2 other closely-related Salmonella serovars as outgroups (S. Kentucky and S. Cubana), which were also shot-gun sequenced. A whole genome single nucleotide polymorphism (SNP) analysis was performed using a maximum likelihood approach to infer phylogenetic relationships. Several monophyletic lineages of S. Tennessee with limited SNP variability were identified that recapitulated several food contamination events. S. Tennessee clades were separated from outgroup salmonellae by more than sixteen thousand SNPs. Intra-serovar diversity of S. Tennessee was small compared to the chosen outgroups (1,153 SNPs), suggesting recent divergence of some S. Tennessee clades. Analysis of all 1,153 SNPs structuring an S. Tennessee peanut butter outbreak cluster revealed that isolates from several food, plant, and clinical isolates were very closely related, as they had only a few SNP differences between them. SNP-based cluster analyses linked specific food sources to several clinical S. Tennessee strains isolated in separate contamination events. Environmental and clinical isolates had very similar whole genome sequences; no markers were found that could be used to discriminate between these sources. Finally, we identified SNPs within variable S. Tennessee genes that may be useful markers for the development of rapid surveillance and typing methods, potentially aiding in traceback efforts during future outbreaks. Using WGS can delimit contamination sources for foodborne illnesses across multiple outbreaks and reveal otherwise undetected DNA sequence differences essential to the tracing of bacterial pathogens as they emerge.

Project description:BackgroundSalmonella spp. is a major foodborne pathogen with a wide variety of serovars associated with human cases and food sources. Nevertheless, in Europe a panel of ten serovars is responsible for up to 80% of confirmed human cases. Clustering studies by single nucleotide polymorphism (SNP) core-genome phylogenetic analysis of outbreaks due to these major serovars are simplified by the availability of many complete genomes in the free access databases. This is not the case for outbreaks due to less common serovars, such as Welikade, for which no reference genomes are available. In this study, we propose a method to solve this problem. We propose to perform a core genome MLST (cgMLST) analysis based on hierarchical clustering using the free-access EnteroBase to select the most suitable genome to use as a reference for SNP phylogenetic analysis. In this study, we applied this protocol to a retrospective analysis of a Salmonella enterica serovar Welikade (S. Welikade) foodborne outbreak that occurred in France in 2016. Finally, we compared the cgMLST and SNP analyses. SNP phylogenetic reconstruction was carried out considering the effect of recombination events identified by the ClonalFrameML tool. The accessory genome was also explored by phage content and virulome analyses.ResultsOur findings revealed high clustering concordance using cgMLST and SNP analyses. Nevertheless, SNP analysis allowed for better assessment of the genetic distance among strains. The results revealed epidemic clones of S. Welikade circulating within the poultry and dairy sectors in France, responsible for sporadic and non-sporadic human cases between 2012 and 2019.ConclusionsThis study increases knowledge on this poorly described serovar and enriches public genome databases with 42 genomes from human and non-human S. Welikade strains, including the isolate collected in 1956 in Sri Lanka, which gave the name to this serovar. This is the first genomic analysis of an outbreak due to S. Welikade described to date.

Project description:Foodborne outbreaks caused by Campylobacter jejuni have become a significant public health problem worldwide. Applying genomic sequencing as a routine part of foodborne outbreak investigation remains in its infancy in China. We applied both traditional PFGE profiling and genomic investigation to understand the cause of a foodborne outbreak in Hangzhou in December 2018. A total of 43 fecal samples, including 27 sick patients and 16 canteen employees from a high school in Hangzhou city in Zhejiang province, were recruited. Routine real-time fluorescent PCR assays were used for scanning the potential infectious agents, including viral pathogens (norovirus, rotavirus, adenovirus, and astrovirus), and bacterial pathogens (Salmonella, Shigella, Campylobacter jejuni, Vibrio parahaemolyticus and Vibrio cholerae). Bacterial selection medium was used to isolate and identify the positive bacteria identified by molecular test. Pulsed field gel electrophoresis (PFGE), and next generation sequencing (NGS) were applied to fifteen recovered C. jejuni isolates to further understand the case linkage of this particular outbreak. Additionally, we retrieved reference genomes from the NCBI database and performed a comparative genomics analysis with the examined genomes produced in this study. The analyzed samples were found to be negative for the queried viruses. Additionally, Salmonella, Shigella, Vibrio parahaemolyticus and Vibrio cholera were not detected. Fifteen C. jejuni strains were identified by the real-time PCR assay and bacterial selection medium. These C. jejuni strains were classified into two genetic profiles defined by the PFGE. Out of fifteen C. jejuni strains, fourteen have a unified consistent genotype belonging to ST2988, and the other strain belongs to ST8149, with a 66.7% similarity in comparison with the rest of the strains. Moreover, all fifteen strains harbored blaOXA-61 and tet(O), in addition to a chromosomal mutation in gyrA (T86I). The examined fourteen strains of ST2988 from CC354 clone group have very minimal genetic difference (3~66 SNPs), demonstrated by the phylogenomic investigation. Both genomic investigation and PFGE profiling confirmed that C. jejuni ST2988, a new derivative from CC354, was responsible for the foodborne outbreak Illustrated in this study.

Project description:Whole-genome sequencing (WGS) is the method of choice for bacterial subtyping and it is rapidly replacing the more traditional methods such as pulsed-field gel electrophoresis (PFGE). Here we used the high-resolution core genome single nucleotide variant (cgSNV) typing method to characterize clinical and food from Salmonella enterica serovar Heidelberg isolates in the context of source attribution. Additionally, clustered regularly interspaced short palindromic repeats (CRISPR) analysis was included to further support this method. Our results revealed that cgSNV was highly discriminatory and separated the outbreak isolates into distinct clusters (0-4 SNVs). CRISPR analysis was also able to distinguish outbreak strains from epidemiologically unrelated isolates. Specifically, our data clearly demonstrated the strength of these two methods to determine the probable source(s) of a 2012 epidemiologically characterized outbreak of S. Heidelberg. Using molecular cut-off of 0-10 SNVs, the cgSNV analysis of 246 clinical and food isolates of S. Heidelberg collected in Québec, in the same year of the outbreak event, revealed that retail and abattoir chicken isolates likely represent an important source of human infection to S. Heidelberg. Interestingly, the isolates genetically related by cgSNV also harbored the same CRISPR as outbreak isolates and clusters. This indicates that CRISPR profiles can be useful as a complementary approach to determine source attribution in foodborne outbreaks. Use of the genomic analysis also allowed to identify a large number of cases that were missed by PFGE, indicating that most outbreaks are probably underestimated. Although epidemiological information must still support WGS-based results, cgSNV method is a highly discriminatory method for the resolution of outbreak events and the attribution of these events to their respective sources. CRISPR typing can serve as a complimentary tool to this analysis during source tracking.

Dataset Information

Whole-Genome Sequencing and Bioinformatic Analysis of Isolates from Foodborne Illness Outbreaks of Campylobacter jejuni and Salmonella enterica.

Publications

Whole-Genome Sequencing and Bioinformatic Analysis of Isolates from Foodborne Illness Outbreaks of Campylobacter jejuni and Salmonella enterica.

Similar Datasets

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