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

Abstract

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.

Introduction

Foodborne salmonellosis is an important concern for public health. It is caused by the enteric pathogen Salmonella enterica, which includes more than 2600 serovars [1]. Human Salmonella infections are classically divided into diseases caused by typhoidal or non-typhoidal salmonella (NTS). Typhoid fever is caused by the human restricted Salmonella enterica serovars Typhi and Paratyphi [2]. Although non-typhoidal Salmonella (NTS) serovars, predominantly cause a self-limiting diarrhoeal illness they have adapted to cause invasive extra-intestinal disease known as invasive NTS (iNTS) which can result in bacteraemia and focal systemic infections [3, 4] . There are two licenced vaccines for prevention of typhoid fever however, they are not effective against NTS [5] moreover, management of iNTS illness is complicated by the emergence of multidrug resistant (MDR) strains [6]. Salmonella serovars responsible for typhoid fever kill over 250,000 humans per year [7] while non-typhoidal Salmonella (NTS) serovars responsible for diarrhoeal illness cause over 155,000 deaths annually [8]. Interestingly, NTS have adapted to cause febrile bacteraemia and serious systemic infections; it has been estimated that over 680,000 people die every year as a result of infection by invasive NTS (iNTS) [3]. Salmonella Typhimurium and Salmonella Dublin have been associated with systemic illness [4, 5]. Human outbreaks of Salmonella Typhimurium and Salmonella Dublin were reported in developed countries [9,10,11].

Conventional laboratory methods such as pulsed field gel electrophoresis (PFGE) do not usually provide adequate discrimination among outbreak and non-outbreak strains of Salmonella enterica and have their limitations in epidemiological surveillance, it is therefore crucial to use accurate, reliable and highly discriminative subtyping methods for epidemiological characterisation and outbreak investigation.

Here, we evaluate different whole genome sequence (WGS)-based subtyping methods (including single nucleotide polymorphism (SNP)-based cluster analysis, multilocus sequence typing (MLST), ribosomal MLST (rMLST), whole genome MLST (wgMLST), core genome MLST (cgMLST) as well as clustered regularly interspaced short palindromic repeats (CRISPRs), prophage sequence profiling, antibiotic resistance profile and plasmid typing) for retrospective investigation of two outbreaks of Salmonella Typhimurium and Salmonella Dublin that occurred in 2013 in UK and Ireland respectively [9, 12].

Methods

Retrospective analyses of the two outbreaks of Salmonella Typhimurium and Salmonella Dublin

We carried out retrospective investigation of a human outbreak of Salmonella Dublin that occurred in 2013 in Ireland [9] and another human outbreak of Salmonella Typhimurium occurred in 2013 in UK [12]. We included suspected food strains isolated from mayonnaise and raw-milk cheeses that can be linked to the outbreaks of Salmonella Typhimurium and Salmonella Dublin respectively. Non-outbreak strains were also included for comparison. Details of all Salmonella Dublin and Salmonella Typhimurium isolates analysed in this study are provided in supplementary Tables 1 and 2 respectively.

PFGE was of a limited value for the investigation of the outbreak of Salmonella Dublin [9] since all outbreak and non-outbreak isolates of Salmonella Dublin were indistinguishable by PFGE. Although multiple loci VNTR analysis (MLVA) was of value in discriminating the outbreak strains from an epidemiologically unrelated isolate in 2013 it was not able to provide a conclusive link between the outbreak strain and a historical isolate from 2011 (11F310) since all outbreak strains had the same MLVA pattern (3-6-1-10-2-3-12) and the historical isolate had similar MLVA pattern (3–6–1-10-2-3-11/12).

Despite the technical limitation of phage typing, it was of value for investigating the outbreak of Salmonella Typhimurium [12] and confirming that mayonnaise is the source of infection.

Denovo assembly of WGS data of Salmonella Dublin and Salmonella Typhimurium strains

We carried out denovo assembly for the raw Fastq paired end (PE) reads for all Salmonella Dublin and Salmonella Typhimurium strains using two different assemblers including Velvet available at Centre for genomic epidemiology (CGE) (http://www.genomicepidemiology.org/) and SPAdes available at Enterobase (http://enterobase.warwick.ac.uk/). We then assessed the quality of the assembly for each strain was assessed using Quast assessment tool (http://quast.bioinf.spbau.ru/).

SNP typing analyses of Salmonella Dublin and Salmonella Typhimurium outbreaks

SNP analysis was carried out using CSIPhylogeny (https://cge.cbs.dtu.dk/services/CSIPhylogeny/) where raw reads were mapped to reference sequences (strain LT2 of Salmonella Typhimurium; accession number: AE006468 and strain CT_02021853 of Salmonella Dublin; accession number: CP001144) using BWA software (http://bio-bwa.sourceforge.net). The depth at each mapped position was calculated using genomeCoverageBed, which is part of BEDTools (https://bedtools.readthedocs.io/en/latest/). High quality SNPs were called using mpileup which is part of SAMTools (http://samtools.sourceforge.net). Genome mappings were then compared and an alignment of the SNPs are then created by concatenating the SNPs. A maximum likelihood (ML) phylogenetic tree was then created based on the concatenated alignment of the high quality SNPs.

Determination of MLST, rMLST, cgMLST and wgMLST of Salmonella Dublin and Salmonella Typhimurium strains

The assembled sequences of each strain were analyzed to detect the MLST, rMLST, cgMLST and wgMLST available at Enetrobase (http://enterobase.warwick.ac.uk/) and CGE (http://www.genomicepidemiology.org/).

Determination of prophage sequence profiles in Salmonella Dublin and Salmonella Typhimurium genomes

Prophages were determined with the draft genomes generated by Velevt and SPAdes for all Salmonella Dublin and Salmonella Typhimurium strains using PHASTER (http://phaster.ca/).

We then used CSI phylogeny available at CGE (http://www.genomicepidemiology.org/) to construct a phylogenetic tree based on the SNPs of detected prophages. Phylogenetic trees were constructed using assembled genomes generated by Velvet and SPAdes assemblers to check if the assembly could affect the tree.

Determination of CRISPRs within Salmonella Dublin and Salmonella Typhimurium strains

Spacers sequence within the draft genomes of all Salmonella Dublin and Salmonella Typhimurium strains were characterized using CRISPRFinder (http://crispr.i2bc.paris-saclay.fr/Server/).

Determination of plasmids within Salmonella Dublin and Salmonella Typhimurium strains

We determined the plasmids within the draft genomes of all Salmonella Dublin and Salmonella Typhimurium strains using the plasmid database; PLSDB (https://ccb-microbe.cs.uni-saarland.de/plsdb/).

In silico analyses of antibiotic resistance within Salmonella Dublin and Salmonella Typhimurium strains

We determined acquired antibiotic resistance genes and mutations within the draft genomes of all Salmonella Dublin and Salmonella Typhimurium strains using ResFinder (https://cge.cbs.dtu.dk/services/ResFinder/).

Results

WGS-based subtyping

SNP based cluster analyses

SNP based tree showed conclusively that the outbreak strains of Salmonella Typhimurium were grouped together in two clades and they are very closely related to strains isolated from mayonnaise (Fig. 1) confirming the source of outbreak is due to consumption of contaminated mayonnaise.

Fig. 1
figure1

Maximum likelihood phylogenetic tree of Salmonella Typhiurium strains based on single nucleotide polymorphisms determined from whole genome sequences. The scale represents the number of nucleotide substitutions per site. Bootstrap support values, given as a percentage of 1000 replicates, are shown on the branches. The tree shows conclusively that myonaise (marked with arrows) is the source of outbreak

The outbreak isolates of Salmonella Dublin were closely related to each other (Fig. 2) and distinct from the non-outbreak isolates that were not readily distinguishable by PFGE. However, the source of Salmonella Dublin outbreak could not be determined and outbreak isolates showed high genetic divergence from the raw-milk cheese isolates related to other outbreaks occurred in France [10].

Fig. 2
figure2

Maximum likelihood phylogenetic tree of Salmonella Dublin strains based on single nucleotide polymorphisms determined from whole genome sequences. The scale represents the number of nucleotide substitutions per site. Bootstrap support values, given as a percentage of 1000 replicates, are shown on the branches. All Salmonella Dublin isolates had indistinguishable pulsed-field gel electrophoresis profiles. Confirmed outbreak cases (n = 9) in October–November 2013 are grouped together in one cluster. However, the source of the outbreak could not be determined as outbreak isolates showed high genetic divergence to bacterial strains isolated from the raw-milk cheeses (marked with arrows) including isolate 2014SAL02972 from Morbier cheese (accession number; ERS2767809) and isolate 2015LSAL00258 from St. Nectaire cheese (accession number: ERS2767808)

MLST, rMLST, cgMLST and wgMLST

As illustrated in Table 1, all Salmonella Dublin strains including the outbreak and non-outbreak strains showed identical MLST (type 10). Interestingly, outbreak isolates of Salmonella Dublin displayed identical rMLST (type 1429) however, some of the non-outbreak strains showed the same rMLST. Moreover, the wgMLST was different among the outbreak strains however, the cgMLST was unique among outbreak strains and can easily separate the outbreak strain from the non-outbreak strains including the 2011 historical isolate (11F310).

Table 1 MLST, rMLST, cgMLST and wgMLST results of Salmonella Dublin outbreak and non-outbreak strains

On the other hand, MLST, rMLST, cgMLST and wgMLST could not discriminate between the outbreak and non-outbreak strains of Salmonella Typhimurium as illustrated in Table 2.

Table 2 MLST, rMLST, cgMLST and wgMLST results of Salmonella Typhimurium outbreak and non-outbreak strains

CRISPR typing

All Salmonella Dublin isolates including outbreak and non-outbreak strains harbour one CRISPR locus and we observed 3 to 5 unique spacers for CRISPR1 locus. Identical spacers were detected among the outbreak and non-outbreak strains as shown in Table 3.

Table 3 Number of spacers within CRISPR1 locus in all Salmonella Dublin strains analysed in this study

Interestingly, the number of spacers in three isolates (517,138, MF7067 and W151R0) changed from (4 spacers) based on Velvet to (5 spacers) based on SPAdes.

All Salmonella Typhimurium isolates harbour 3 CRISPR loci. Identical spacers were detected among the outbreak and non-outbreak strains as shown in Table 4. There was no difference between the numbers of spacers using different assemblers.

Table 4 Number of spacers within CRISPRs loci in all Salmonella Typhimurium strains analysed in this study

Prophage sequence profiling

All Salmonella Dublin strains including the outbreak strains are lysogenic for three prophages (Gifsy_2, 118970_sal3 and RE_2010). However, phylogenetic analyses of Salmonella Dublin strains based on the SNPs of prophages showed that outbreak strains are intermixed with the non-outbreak strains based on velvet assembler (Fig. 3) and SPAdes assembler (Fig. 4).

Fig. 3
figure3

Maximum likelihood phylogenetic tree of Salmonella Dublin strains based on prophages SNPs using Velvet

Fig. 4
figure4

Maximum likelihood phylogenetic tree of Salmonella Dublin strains based on prophages SNPs using SPAdes

All Salmonella Typhimurium genomes assembled by SPAdes revealed the presence of four prophages in all outbreak and non-outbreak strains including the three Salmonella prophages (Gifsy 2, RE-2010, and 118970_sal3) and the Edwardsiella specific phage (GF-2).

On the other hand, Salmonella Typhimurium genomes assembled by Velvet were lysogenic for two Salmonella specific prophages (Gifsy 2 and RE-2010). All strains except one outbreak isolate (H132940750) harbour Salmonella 118970_sal3 phage.

Interestingly, all strains harbour Edwardsiella GF-2 prophage except three outbreak isolates (H132940748, H133000645 and H133060376).

Phylogenetic analyses of Salmonella Typhimurium strains based on the SNPs of prophages showed that outbreak strains are intermixed with the non-outbreak strains using velvet assembler (Fig. 5) and using SPAdes assembler (Fig. 6).

Fig. 5
figure5

ML phylogenetic tree of Salmonella Typhimurium strains based on prophages SNPs using Velvet

Fig. 6
figure6

ML phylogenetic tree of Salmonella Typhimurium strains based on prophages SNPs using SPAdes

Plasmid typing

All outbreak and non-outbreak strains of Salmonella Dublin harbour identical plasmid type (except three non-outbreak isolates; M1314220, MB12371 and B261193) as shown in Table 5.

Table 5 Distribution of plasmids among outbreak and non-outbreak strains of Salmonella Dublin

Same plasmids were determined using Velvet and SPAdes assemblers.

All outbreak and non-outbreak isolates of Salmonella Typhimurium harbour 3 plasmids (pATCC14028, plasmid: 4 and pSE81–1705) except the outbreak strain H133300609 which did not carry plasmid pATCC14028 but it harbours a different plasmid (pSLT_VNP20009) instead (Table 6).

Table 6 Distribution of plasmids among outbreak and non-outbreak strains of Salmonella Typhimurium

Antibiotic resistance profile

All Salmonella Dublin isolates including the outbreak and non-outbreak strains are resistant to aminoglycosides due to the acquisition of the aac(6′)-Iaa gene. No mutations were detected against gyrA and parC genes in all isolates except one isolate (MF038630) that carried a non-synonyms mutation within the gyrase protein and it is associated with bacterial resistance to nalidixic acid (Table 7).

Table 7 In silico analyses results of antimicrobial resistance genes and mutations within all Salmonella Dublin strains

All the Salmonella Typhimurium isolates of both the outbreak and non-outbreak group are resistant to aminoglycosides due to the acquisition of the “aac(6′)-Iaa gene”. No known mutations were detected against gyrA and parC (Table 8).

Table 8 In silico analyses results of antimicrobial resistance genes and mutations within all Salmonella Typhimurium strains

Discussion

Salmonellosis is one of the most common foodborne diseases worldwide and has been associated with high morbidity and mortality rates. It is estimated that over 680,000 humans throughout the world are killed each year by iNTS. The most predominant iNTS serovars are Typhimurium, Enteritidis and Dublin [13, 14]. It is therefore very crucial to use accurate, reliable and highly discriminative subtyping methods for epidemiological surveillance and outbreak investigation.

Although PFGE is considered as current gold standard for all Salmonella serotypes, it has its limitations moreover, variation between laboratories has been reported when identifying the source of infection and discriminating between the outbreak and non-outbreak isolates [15].

Other phenotypic tools such as phage typing and antimicrobial resistance profiling have been crucial in the outbreak investigations [15, 16]. Furthermore, MLVA has been used to distinguish between genetically closely related strains and trace back the sources of disease outbreaks related to food [15, 17].

Genotypic approaches have ameliorated the methods for carrying out outbreak investigation and epidemiological surveillance [18]. The advent of whole genome sequencing (WGS) has opened the possibilities to enhance the typing approaches for outbreak investigation and epidemiological surveillance. In our study, WGS data have been analyzed to test the suitability of different approaches as subtyping tool for Salmonella enterica surveillance. We therefore carried out retrospective investigation of two different outbreaks of Salmonella Typhimurium and Salmonella Dublin that occurred in 2013 in UK and Ireland respectively [6, 19] using different WGS-subtyping methods.

In this study, 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. Although SNP-analyses of Salmonella Dublin genomes confirmed the outbreak, however the source of infection could not be determined.

On the other the WGS-subtyping methods including MLST, rMLST, wgMLST, cgMLST showed limited discrimination for the outbreak and non-outbreak isolates of Salmonella Typhimurium strains. However, cgMLST defined the genetic relatedness among Salmonella Dublin isolates more precisely and confirmed there is no relation among the 2013 outbreak isolates and the 2011 historical isolate (11F310) of Salmonella Dublin.

It was reported that MLST might not be the most suitable epidemiological tool [20] but it is best for analyzing the genetic diversity of the strain and analyze the core and conserved genes of pathogens that are of public importance.

The cgMLST bridges the classic MLST with the novel WGS-based approach since it combines the discriminatory power of MLST with large-scale data obtained from WGS enabling to exploit a considerable number of gene targets throughout the bacterial genome which would maximize the quality and resolution for surveillance and research works.

A recent study showed that cgMLST has shown the robustness of cgMLST as a tool to investigate multi-country outbreak of Salmonella Enteritidis in Europe [21].

The difference between the cgMLST and wgMLST is that unlike cgMLST, wgMLST indexes the variation of pre-defined set of genes from both core and accessory genes [22]. Another retrospective study on 8 different outbreaks associated with verotoxigenic Escherichia coli (VTEC) O157:H7 in Canada showed that wgMLST provided higher discrimination than PFGE and MLVA [23].

Research studies have shown that cgMLST and wgMLST are viable typing methods for outbreak surveillance. In our study, cgMLST proved to provide higher discriminatory resolution for differentiating Salmonella Dublin isolates of outbreak group from the non-outbreak group. However, both cgMLST and wgMLST were unsuccessful in differentiating outbreak-related Salmonella Typhimurium isolates from outbreak-unrelated isolates.

Bacterial genome comprises a considerable amount (10 to 20%) of prophages integrated in their core genome [24]. Prophages harbor genes for antimicrobial resistance, virulence and toxins which contribute to the genetic diversity of bacterial strains making prophages a potential marker for discriminating Salmonella serovars [25]. However, one of the limitations of using prophage sequence profiles for Salmonella subtyping is the sensitivity and accuracy of the assembly as some prophage regions might be lost during assembly. We used two different denovo assemblers (SPAdes and Velvet) and found that prophage sequence profiling could not differentiate between the outbreak and non-outbreak isolates.

Recent studies have suggested that high throughput CRISPR typing has the potential to be used for epidemiological surveillance and investigation of Salmonella outbreaks [26, 27]. However, in our study, we detected identical spacers among outbreak and non-outbreak associated strains indicating that CRISPR typing is not useful for the surveillance of Salmonella enetrica outbreaks as we showed in our previous studies [28, 29] however, it might be useful for the discrimination among different Salmonella serovars.

Plasmid profiles and antimicrobial- susceptibility profiling have been used as an epidemiological tool since many decades. However, it was reported that analysis of plasmid profiles provided higher discrimination in the outbreak investigations than analysis of antimicrobial-susceptibility pattern [30, 31]. In our study both plasmid typing and in silico analysis of antibiotic resistance were unable to discriminate between the outbreak isolates and non-outbreak isolates.

In this study, we compared several retrospective WGS-based subtyping methods and we showed that SNP-based cluster analysis is superior to other subtying methods to define the source of outbreak in real-time.

In conclusion, 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. The rapid development of next-generation sequencing (NGS) technology and bioinformatics tools have enabled WGS of any bacterial strain feasible. Various typing tools have been proposed by using WGS data but currently, the adoption of WGS-based methods have proved to be difficult due to lack of standardization. There are many layers on obtaining WGS data and there is need of standardization from the type of sequencers used to the bioinformatics analysis. Therefore, the emerging genetic analysis techniques should be combined with conventional phenotypic and molecular methods for routine surveillance and outbreak investigation until the WGS-based methods can be fully exploited, improved and standardized.

Availability of data and materials

Available in supplementary Tables 1 and 2.

Abbreviations

CGE:

Centre for Genomic Epidemiology

cgMLST:

Core genome multilocus sequence typing

CRISPRs:

Clustered regularly interspaced short palindromic repeats

iNTS:

Invasive NTS

ML:

Maximum likelihood

MLST:

Multilocus sequence typing

MLVA:

Multiple loci VNTR analysis

NGS:

Next generation sequencing

NTS:

Non-typhoidal Salmonella

PE:

Paired end

PFGE:

Pulsed field gel electrophoresis

rMLST:

Ribosomal MLST

SNP:

Single nucleotide polymorphism

wgMLST:

Whole genome MLST

WGS:

Whole genome sequence

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Acknowledgements

Authors would like to thank Falling Walls Lab London for the invitation to present this research work. Dr Manal Mohammed is funded by a Quinton Hogg start-up award, University of Westminster.

Funding

MM is funded by a Quinton Hogg start-up award, University of Westminster.

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Contributions

MM: designed the study, contributed to data analyses, wrote the initial draft of the manuscript. ST: contributed to data analyses and contributed to manuscript writing. All authors reviewed and edited the manuscript. The authors read and approved the final manuscript.

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MM is a lecturer of Medical Microbiology, School of Life Sciences, University of Westminster. ST is a MSc Medical Microbiology student at School of Life Sciences, University of Westminster.

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Correspondence to Manal Mohammed.

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Supplementary information

Additional file 1: Supplementary Table 1.

Details of Salmonella Dublin strains analysed in this study. Supplementary Table 2. Details of Salmonella Typhimurium strains analysed in this study

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Mohammed, M., Thapa, S. Evaluation of WGS-subtyping methods for epidemiological surveillance of foodborne salmonellosis. One Health Outlook 2, 13 (2020). https://doi.org/10.1186/s42522-020-00016-5

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Keywords

  • Salmonella
  • WGS
  • Subtyping
  • SNP-typing
  • Prophage profile
  • CRISPR typing
  • MLST
  • rMLST
  • wgMLST
  • cgMLST