The Validation and Implications of Using Whole Genome Sequencing as a Replacement for Traditional Serotyping for a National Salmonella Reference Laboratory

The integrated genomic surveillance system of Andalusia (SIEGA) provides a One Health regional resource connected with the clinic

The One Health approach, recognizing the interconnectedness of human, animal, and environmental health, has gained significance amid emerging zoonotic diseases and antibiotic resistance concerns. This paper aims to demonstrate the utility of a collaborative tool, the SIEGA, for monitoring infectious diseases across domains, fostering a comprehensive understanding of disease dynamics and risk factors, highlighting the pivotal role of One Health surveillance systems. Raw whole-genome sequencing is processed through different species-specific open software that additionally reports the presence of genes associated to anti-microbial resistances and virulence. The SIEGA application is a Laboratory Information Management System, that allows customizing reports, detect transmission chains, and promptly alert on alarming genetic similarities. The SIEGA initiative has successfully accumulated a comprehensive collection of more than 1900 bacterial genomes, including Salmonella enterica, Listeria monocytogenes, Campylobacter jejuni, Escherichia coli, Yersinia enterocolitica and Legionella pneumophila, showcasing its potential in monitoring pathogen transmission, resistance patterns, and virulence factors. SIEGA enables customizable reports and prompt detection of transmission chains, highlighting its contribution to enhancing vigilance and response capabilities. Here we show the potential of genomics in One Health surveillance when supported by an appropriate bioinformatic tool. By facilitating precise disease control strategies and antimicrobial resistance management, SIEGA enhances global health security and reduces the burden of infectious diseases. The integration of health data from humans, animals, and the environment, coupled with advanced genomics, underscores the importance of a holistic One Health approach in mitigating health threats.

Genomic Surveillance of Salmonella from the Comunitat Valenciana (Spain)

Salmonella enterica subspecies enterica is one of the most important foodborne pathogens and the causative agent of salmonellosis, which affects both humans and animals producing numerous infections every year. The study and understanding of its epidemiology are key to monitoring and controlling these bacteria. With the development of whole-genome sequencing (WGS) technologies, surveillance based on traditional serotyping and phenotypic tests of resistance is being replaced by genomic surveillance. To introduce WGS as a routine methodology for the surveillance of food-borne Salmonella in the region, we applied this technology to analyze a set of 141 S. enterica isolates obtained from various food sources between 2010 and 2017 in the Comunitat Valenciana (Spain). For this, we performed an evaluation of the most relevant Salmonella typing methods, serotyping and sequence typing, using both traditional and in silico approaches. We extended the use of WGS to detect antimicrobial resistance determinants and predicted minimum inhibitory concentrations (MICs). Finally, to understand possible contaminant sources in this region and their relationship to antimicrobial resistance (AMR), we performed cluster detection combining single-nucleotide polymorphism (SNP) pairwise distances and phylogenetic and epidemiological data. The results of in silico serotyping with WGS data were highly congruent with those of serological analyses (98.5% concordance). Multi-locus sequence typing (MLST) profiles obtained with WGS information were also highly congruent with the sequence type (ST) assignment based on Sanger sequencing (91.9% coincidence). In silico identification of antimicrobial resistance determinants and minimum inhibitory concentrations revealed a high number of resistance genes and possible resistant isolates. A combined phylogenetic and epidemiological analysis with complete genome sequences revealed relationships among isolates indicative of possible common sources for isolates with separate sampling in time and space that had not been detected from epidemiological information. As a result, we demonstrate the usefulness of WGS and in silico methods to obtain an improved characterization of S. enterica enterica isolates, allowing better surveillance of the pathogen in food products and in potential environmental and clinical samples of related interest.

Lytic bacteriophage vB_KmiS-Kmi2C disrupts biofilms formed by members of the Klebsiella oxytoca complex, and represents a novel virus family and genus

AIMS

This study aimed to characterize the lytic phage vB_KmiS-Kmi2C, isolated from sewage water on a GES-positive strain of Klebsiella michiganensis.

METHODS AND RESULTS

Comparative phylogenetic and network-based analyses were used to characterize the genome of phage vB_KmiS-Kmi2C (circular genome of 42 234 bp predicted to encode 55 genes), demonstrating it shared little similarity with other known phages. The phage was lytic on clinical strains of K. oxytoca (n = 2) and K. michiganensis (n = 4), and was found to both prevent biofilm formation and disrupt established biofilms produced by these strains.

CONCLUSIONS

We have identified a phage capable of killing clinically relevant members of the K. oxytoca complex (KoC). The phage represents a novel virus family (proposed name Dilsviridae) and genus (proposed name Dilsvirus).

Comparing Multiple Locus Variable-Number Tandem Repeat Analyses with Whole-Genome Sequencing as Typing Method for Salmonella Enteritidis Surveillance in The Netherlands, January 2019 to March 2020

In the Netherlands, whole-genome sequencing (WGS) was implemented as routine typing tool for Salmonella Enteritidis isolates in 2019. Multiple locus variable-number tandem repeat analyses (MLVA) was performed in parallel. The objective was to determine the concordance of MLVA and WGS as typing methods for S. Enteritidis isolates. We included S. Enteritidis isolates from patients that were subtyped using MLVA and WGS-based core-genome Multilocus Sequence Typing (cgMLST) as part of the national laboratory surveillance of Salmonella during January 2019 to March 2020. The concordance of clustering based on MLVA and cgMLST, with a distance of ≤5 alleles, was assessed using the Fowlkes-Mallows (FM) index, and their discriminatory power using Simpson's diversity index. Of 439 isolates in total, 404 (92%) were typed as 32 clusters based on MLVA, with a median size of 4 isolates (range:2 to 141 isolates). Based on cgMLST, 313 (71%) isolates were typed as 48 clusters, with a median size of 3 isolates (range:2 to 39 isolates). The FM index was 0.34 on a scale from 0 to 1, where a higher value indicates greater similarity between the typing methods. The Simpson's diversity index of MLVA and cgMLST was 0.860 and 0.974, respectively. The median cgMLST distance between isolates with the same MLVA type was 27 alleles (interquartile range [IQR]:17 to 34 alleles), and 2 alleles within cgMLST clusters (IQR:1-5 alleles). This study shows the higher discriminatory power of WGS over MLVA and a poor concordance between both typing methods regarding clustering of S. Enteritidis isolates. IMPORTANCE Salmonella is the most frequently reported agent causing foodborne outbreaks and the second most common zoonoses in the European Union. The incidence of the most dominant serotype Enteritidis has increased in recent years. To differentiate between Salmonella isolates, traditional typing methods such as pulsed-field gel electrophoresis (PFGE) and multiple locus variable-number tandem repeat analyses (MLVA) are increasingly replaced with whole-genome sequencing (WGS). This study compared MLVA and WGS-based core-genome Multilocus Sequence Typing (cgMLST) as typing tools for S. Enteritidis isolates that were collected as part of the national Salmonella surveillance in the Netherlands. We found a higher discriminatory power of WGS-based cgMLST over MLVA, as well as a poor concordance between both typing methods regarding clustering of S. Enteritidis isolates. This is especially relevant for cluster delineation in outbreak investigations and confirmation of the outbreak source in trace-back investigations.

Phenotypic and genotypic characterization of antimicrobial resistance profiles in Salmonella isolated from waterfowl in 2002–2005 and 2018–2020 in Sichuan, China

Salmonella enterica is a widespread foodborne pathogen with concerning antimicrobial resistance (AMR). Waterfowl are a major source of Salmonella transmission, but there are few systematic studies on Salmonella prevalence in waterfowl species. In this study, 126 Salmonella isolates (65 collected in 2018–2020 and 61 collected in 2002–2005) were obtained from waterfowl samples in Sichuan, China. Their serotypes, pulsed-field gel electrophoresis (PFGE) types, and phenotypic and genotypic AMR profiles were systematically examined. The isolates were distributed in 7 serotypes, including serovars Enteritidis (46.0%), Potsdam (27.8%), Montevideo (7.9%), Cerro (6.3%), Typhimurium (4.8%), Kottbus (4.0%) and Apeyeme (3.2%). Their PFGE characteristics were diverse; all isolates were distributed in four groups (cutoff value: 60.0%) and 20 clusters (cutoff value: 80.0%). Moreover, all isolates were multidrug resistant, and high rates of AMR to lincomycin (100.0%), rifampicin (100.0%), sulfadiazine (93.7%), erythromycin (89.7%), ciprofloxacin (81.0%), and gentamicin (75.4%) were observed. Finally, 49 isolates were subjected to whole-genome sequencing, and a wide variety of AMR genes were found, including multiple efflux pump genes and specific resistance genes. Interestingly, the tet(A)/tet(B) and catII resistance genes were detected in only isolates obtained in the first collection period, while the gyrA (S83F, D87N and D87G) and gyrB (E466D) mutations were detected at higher frequencies in the isolates obtained in the second collection period, supporting the findings that isolates from different periods exhibited different patterns of resistance to tetracycline, chloramphenicol and nalidixic acid. In addition, various incompatible plasmid replicon fragments were detected, including Col440I, Col440II, IncFIB, IncFII, IncX1, IncX9, IncI1-I and IncI2, which may contribute to the horizontal transmission of AMR genes and provide competitive advantages. In summary, we demonstrated that the Salmonella isolates prevalent in Sichuan waterfowl farms exhibited diverse serotypes, multiple AMR phenotypes and genotypes, and AMR changes over time, indicating their potential risks to public health.

Mobility of antimicrobial resistance across serovars and disease presentations in non-typhoidal Salmonella from animals and humans in Vietnam

Non-typhoidal Salmonella (NTS) is a major cause of bacterial enterocolitis globally but also causes invasive bloodstream infections. Antimicrobial resistance (AMR) hampers the treatment of these infections and understanding how AMR spreads between NTS may help in developing effective strategies. We investigated NTS isolates associated with invasive disease, diarrhoeal disease and asymptomatic carriage in animals and humans from Vietnam. Isolates included multiple serovars and both common and rare phenotypic AMR profiles; long- and short-read sequencing was used to investigate the genetic mechanisms and genomic backgrounds associated with phenotypic AMR profiles. We demonstrate concordance between most AMR genotypes and phenotypes but identified large genotypic diversity in clinically relevant phenotypes and the high mobility potential of AMR genes (ARGs) in this setting. We found that 84 % of ARGs identified were located on plasmids, most commonly those containing IncHI1A_1 and IncHI1B(R27)_1_R27 replicons (33%), and those containing IncHI2_1 and IncHI2A_1 replicons (31%). The vast majority (95%) of ARGS were found within 10 kbp of IS6/IS26 elements, which provide plasmids with a mechanism to exchange ARGs between plasmids and other parts of the genome. Whole genome sequencing with targeted long-read sequencing applied in a One Health context identified a comparatively limited number of insertion sequences and plasmid replicons associated with AMR. Therefore, in the context of NTS from Vietnam and likely for other settings as well, the mechanisms by which ARGs move contribute to a more successful AMR profile than the specific ARGs, facilitating the adaptation of bacteria to different environments or selection pressures.

Molecular Characterization of Salmonella Detected along the Broiler Production Chain in Trinidad and Tobago

This cross-sectional study determined the serovars, antimicrobial resistance genes, and virulence factors of Salmonella isolated from hatcheries, broiler farms, processing plants, and retail outlets in Trinidad and Tobago. Salmonella in silico serotyping detected 23 different serovars where Kentucky 20.5% (30/146), Javiana 19.2% (28/146), Infantis 13.7% (20/146), and Albany 8.9% (13/146) were the predominant serovars. There was a 76.0% (111/146) agreement between serotyping results using traditional conventional methods and whole-genome sequencing (WGS) in in silico analysis. In silico identification of antimicrobial resistance genes conferring resistance to aminoglycosides, cephalosporins, peptides, sulfonamides, and antiseptics were detected. Multidrug resistance (MDR) was detected in 6.8% (10/146) of the isolates of which 100% originated from broiler farms. Overall, virulence factors associated with secretion systems and fimbrial adherence determinants accounted for 69.3% (3091/4463), and 29.2% (1302/4463) counts, respectively. Ten of 20 isolates of serovar Infantis (50.0%) showed MDR and contained the bla_CTX-M-65 gene. This is the first molecular characterization of Salmonella isolates detected along the entire broiler production continuum in the Caribbean region using WGS. The availability of these genomes will help future source tracking during epidemiological investigations associated with Salmonella foodborne outbreaks in the region and worldwide.

Genotyping of Listeria monocytogenes isolates by high-resolution melting curve (HRM) analysis of tandem repeat locus

Listeria monocytogenes is responsible for causing listeriosis, a type of food poisoning with high mortality. This bacterium is mainly transmitted to humans through the consumption of contaminated foods. Detection of L. monocytogenes through molecular methods is crucial for food safety and clinical diagnosis. Present techniques are characterized by low discrimination power and high cost, as well as being time-consuming and taking several days to give the final result. In our study, MLVA-HRM (Multiple-Locus Variable-number tandem repeats Analysis ‒ High-Resolution Melting) was investigated as an alternative method for a fast and precise method for the genotyping of L. monocytogenes isolates. Forty-eight isolates of L. monocytogenes obtained from the microbial bank of Department of Microbiology, Iran University of Medical Sciences, were typed by MLVA-HRM analysis using five Variable Numbers of Tandem Repeat (VNTR) loci. A total of 43 different types were obtained. This research demonstrated the usefulness of the MLVA-HRMA method and its ability to discriminate L. monocytogenes isolates. Since this method is easier and more efficient than existing methods, it can be widely used in food processing plants and diagnostic laboratories as a fast and accurate method.

Monitoring the Microevolution of Salmonella enterica in Healthy Dairy Cattle Populations at the Individual Farm Level Using Whole-Genome Sequencing

Livestock represent a possible reservoir for facilitating the transmission of the zoonotic foodborne pathogen Salmonella enterica to humans; there is also concern that strains can acquire resistance to antimicrobials in the farm environment. Here, whole-genome sequencing (WGS) was used to characterize Salmonella strains (n = 128) isolated from healthy dairy cattle and their associated environments on 13 New York State farms to assess the diversity and microevolution of this important pathogen at the level of the individual herd. Additionally, the accuracy and concordance of multiple in silico tools are assessed, including: (i) two in silico serotyping tools, (ii) combinations of five antimicrobial resistance (AMR) determinant detection tools and one to five AMR determinant databases, and (iii) one antimicrobial minimum inhibitory concentration (MIC) prediction tool. For the isolates sequenced here, in silico serotyping methods outperformed traditional serotyping and resolved all un-typable and/or ambiguous serotype assignments. Serotypes assigned in silico showed greater congruency with the Salmonella whole-genome phylogeny than traditional serotype assignments, and in silico methods showed high concordance (99% agreement). In silico AMR determinant detection methods additionally showed a high degree of concordance, regardless of the pipeline or database used (≥98% agreement among susceptible/resistant assignments for all pipeline/database combinations). For AMR detection methods that relied exclusively on nucleotide BLAST, accuracy could be maximized by using a range of minimum nucleotide identity and coverage thresholds, with thresholds of 75% nucleotide identity and 50-60% coverage adequate for most pipeline/database combinations. In silico characterization of the microevolution and AMR dynamics of each of six serotype groups (S. Anatum, Cerro, Kentucky, Meleagridis, Newport, Typhimurium/Typhimurium variant Copenhagen) revealed that some lineages were strongly associated with individual farms, while others were distributed across multiple farms. Numerous AMR determinant acquisition and loss events were identified, including the recent acquisition of cephalosporin resistance-conferring bla CMY- and bla CTX-M-type beta-lactamases. The results presented here provide high-resolution insight into the temporal dynamics of AMR Salmonella at the scale of the individual farm and highlight both the strengths and limitations of WGS in tracking zoonotic pathogens and their associated AMR determinants at the livestock-human interface.

Rapid Real-Time Polymerase Chain Reaction for Salmonella Serotyping Based on Novel Unique Gene Markers by Pangenome Analysis

An accurate diagnostic method for Salmonella serovars is fundamental to preventing the spread of associated diseases. A diagnostic polymerase chain reaction (PCR)-based method has proven to be an effective tool for detecting pathogenic bacteria. However, the gene markers currently used in real-time PCR to detect Salmonella serovars have low specificity and are developed for only a few serovars. Therefore, in this study, we explored the novel unique gene markers for 60 serovars that share similar antigenic formulas and show high prevalence using pangenome analysis and developed a real-time PCR to detect them. Before exploring gene markers, the 535 Salmonella genomes were evaluated, and some genomes had serovars different from the designated serovar information. Based on these analyses, serovar-specific gene markers were explored. These markers were identified as genes present in all strains of target serovar genomes but absent in strains of other serovar genomes. Serovar-specific primer pairs were designed from the gene markers, and a real-time PCR method that can distinguish between 60 of the most common Salmonella serovars in a single 96-well plate assay was developed. As a result, real-time PCR showed 100% specificity for 199 Salmonella and 29 non-Salmonella strains. Subsequently, the method developed was applied successfully to both strains with identified serovars and an unknown strain, demonstrating that real-time PCR can accurately detect serovars of strains compared with traditional serotyping methods, such as antisera agglutination. Therefore, our method enables rapid and economical Salmonella serotyping compared with the traditional serotyping method.

Genomic Epidemiology of Multidrug-Resistant Nontyphoidal Salmonella in Young Children Hospitalized for Gastroenteritis

Nontyphoidal Salmonella (NTS) gastroenteritis in children remains a significant burden on health care and constitutes a majority of all admissions for Salmonella infections in public hospitals in Hong Kong. In this prospective study, 41% of 241 children hospitalized with gastroenteritis from three public hospitals during 2019 were culture confirmed to have NTS infection. These Salmonella isolates were whole-genome sequenced and in silico predicted for their serovars/serotypes using the Salmonella In Silico Typing Resource (SISTR) and SeqSero1, and the antimicrobial resistance (AMR) genes were determined. Phylogenetic analysis revealed three major clades belonging to Salmonella enterica serovar Enteritidis sequence type 11 (ST11) (43%), multidrug-resistant (MDR) S. Typhimurium ST19 (12%) and its monophasic variant ST34 (25%), and mostly singletons of 15 other serovars. MDR S. Typhimurium and its variant were more common in infants <24 months of age and possessed genotypic resistance to five antimicrobial agents, including ampicillin (A), chloramphenicol (C), aminoglycosides (Am), sulfonamides (Su), and tetracyclines (T). Older children were more often infected with S. Enteritidis, which possessed distinct genotypic resistance to AAmSu and fluoroquinolones. In addition, 3% of the isolates possessed extended-spectrum beta-lactamase (ESBL) CTX-M genes, while one isolate (1%) harboring the carbapenemase gene blaNDM-1 was identified. Our findings provide a more complete genomic epidemiological insight into NTS causing gastroenteritis and identify a wider spectrum of determinants of resistance to third-generation beta-lactams and carbapenems, which are often not readily recognized. With high rates of multidrug-resistant NTS from studies in the Asia-Pacific region, the rapid and reliable determination of serovars and resistance determinants using whole-genome sequencing (WGS) is invaluable for enhancing public health interventions for infection prevention and control. IMPORTANCE Nontyphoidal Salmonella (NTS) gastroenteritis is a foodborne disease with a large global burden. Antimicrobial resistance (AMR) among foodborne pathogens is an important public health concern, and multidrug-resistant (MDR) Salmonella is prevalent in Southeast Asia and China. Using whole-genome sequencing, this study highlights the relationship of the MDR Salmonella serotypes and the diverse range of Salmonella genotypes that contaminate our food sources and contribute to disease in this locality. The findings update our understanding of Salmonella epidemiology and associated MDR determinants to enhance the tracking of foodborne pathogens for public health and food safety.

Comparison of Molecular and In Silico Salmonella Serotyping for Salmonella Surveillance

Salmonella surveillance and outbreak management is a key function of public health. Laboratories are shifting from antigenic serotype determination to molecular methods including microarray or whole genome sequencing technologies. The objective of this study was to compare the Check&Trace Salmonella™ DNA microarray (CTS), a commercially available assay with the Salmonella in silico typing resource (SISTR), which uses whole genome sequencing technology for serotyping clinical Salmonella strains in Alberta, Canada, collected over an 18-month period. A high proportion of isolates (96.3%) were successfully typed by both systems. SISTR is a powerful tool for laboratories which already have a WGS infrastructure in place, whereas smaller laboratories can benefit from a commercial microarray system and reduce the processing cost per isolate compared to traditional serotyping.

Molecular Epidemiology of Salmonellosis in Florida, USA, 2017-2018

The state of Florida reports a high burden of non-typhoidal Salmonella enterica with approximately two times higher than the national incidence. We retrospectively analyzed the population structure and molecular epidemiology of 1,709 clinical isolates from 2017 and 2018. We found 115 different serotypes. Rarefaction suggested that the serotype richness did not differ between children under 2 years of age and older children and adults and, there are ~22 well-characterized dominant serotypes. There were distinct differences in dominant serotypes between Florida and the USA as a whole, even though S. Enteritidis and S. Newport were the dominant serotypes in Florida and nationally. S. Javiana, S. Sandiego, and S. IV 50:z4, z23:- occurred more frequently in Florida than nationally. Legacy Multi Locus Sequence Typing (MLST) was of limited use for differentiating clinical Salmonella isolates beyond the serotype level. We utilized core genome MLST (cgMLST) hierarchical clusters (HC) to identify potential outbreaks and compared them to outbreaks detected by Pulse Field Gel Electrophoresis (PFGE) surveillance for five dominant serotypes (Enteritidis, Newport, Javiana, Typhimurium, and Bareilly). Single nucleotide polymorphism (SNP) phylogenetic-analysis of cgMLST HC at allelic distance 5 or less (HC5) corroborated PFGE detected outbreaks and generated well-segregated SNP distance-based clades for all studied serotypes. We propose “combination approach” comprising “HC5 clustering,” as efficient tool to trigger Salmonella outbreak investigations, and “SNP-based analysis,” for higher resolution phylogeny to confirm an outbreak. We also applied this approach to identify case clusters, more distant in time and place than traditional outbreaks but may have been infected from a common source, comparing 176 Florida clinical isolates and 1,341 non-clinical isolates across USA, of most prevalent serotype Enteritidis collected during 2017–2018. Several clusters of closely related isolates (0–4 SNP apart) within HC5 clusters were detected and some included isolates from poultry from different states in the US, spanning time periods over 1 year. Two SNP-clusters within the same HC5 cluster included isolates with the same multidrug-resistant profile from both humans and poultry, supporting the epidemiological link. These clusters likely reflect the vertical transmission of Salmonella clones from higher levels in the breeding pyramid to production flocks.

Classification of Salmonella enterica of the (Para-)Typhoid Fever Group by Fourier-Transform Infrared (FTIR) Spectroscopy

Typhoidal and para-typhoidal Salmonella are major causes of bacteraemia in resource-limited countries. Diagnostic alternatives to laborious and resource-demanding serotyping are essential. Fourier transform infrared spectroscopy (FTIRS) is a rapidly developing and simple bacterial typing technology. In this study, we assessed the discriminatory power of the FTIRS-based IR Biotyper (Bruker Daltonik GmbH, Bremen, Germany), for the rapid and reliable identification of biochemically confirmed typhoid and paratyphoid fever-associated Salmonella isolates. In total, 359 isolates, comprising 30 S. Typhi, 23 S. Paratyphi A, 23 S. Paratyphi B, and 7 S. Paratyphi C, respectively and other phylogenetically closely related Salmonella serovars belonging to the serogroups O:2, O:4, O:7 and O:9 were tested. The strains were derived from clinical, environmental and food samples collected at different European sites. Applying artificial neural networks, specific automated classifiers were built to discriminate typhoidal serovars from non-typhoidal serovars within each of the four serogroups. The accuracy of the classifiers was 99.9%, 87.0%, 99.5% and 99.0% for Salmonella Typhi, Salmonella Paratyphi A, B and Salmonella Paratyphi C, respectively. The IR Biotyper is a promising tool for fast and reliable detection of typhoidal Salmonella. Hence, IR biotyping may serve as a suitable alternative to conventional approaches for surveillance and diagnostic purposes.

Evaluation of nanopore sequencing technology to identify Salmonella enterica Choleraesuis var. Kunzendorf and Orion var. 15+, 34. Int J Food Microbiol 2021;346:109167. [PMID: 33774575 DOI: 10.1016/j.ijfoodmicro.2021.109167]

Our previous study demonstrated that whole genome sequencing (WGS) data generated by Oxford Nanopore Technologies (ONT) can be used for rapid and accurate prediction of selected Salmonella serotypes. However, one limitation is that established methods for WGS-based serotype prediction, utilizing data from either ONT or Illumina, cannot differentiate certain serotypes and serotype variants with the same or closely related antigenic formulae. This study aimed to evaluate nanopore sequencing and additional data analysis for identification of Salmonella enterica Choleraesuis var. Kunzendorf and S. enterica Orion var. 15⁺, 34⁺, thus overcoming this limitation. Five workflows that combined different flow cells, library construction methods and basecaller models were evaluated and compared. The workflow that consisted of the R9 flow cell, rapid sequencing library construction kit and guppy basecaller with base modified model performed best for Single Nucleotide Polymorphism (SNP) analysis. With this workflow, 99.98% of matching identity between assembled genomes from ONT and that from Illumina was achieved. Less than five high-quality SNPs differed when comparing sequencing data between ONT and Illumina. SNP typing successfully identified Choleraesuis var. Kunzendorf. While prophage prediction further differentiated Orion var. 15⁺, 34⁺ from the other two Orion variants. Our study improves the readiness of ONT as a Salmonella subtyping and source tracking tool for food industry applications.

Evaluation of Salmonella Serotype Prediction With Multiplex Nanopore Sequencing

The use of whole genome sequencing (WGS) data generated by the long-read sequencing platform Oxford Nanopore Technologies (ONT) has been shown to provide reliable results for Salmonella serotype prediction in a previous study. To further meet the needs of industry for accurate, rapid, and cost-efficient Salmonella confirmation and serotype classification, we evaluated the serotype prediction accuracy of using WGS data from multiplex ONT sequencing with three, four, five, seven, or ten Salmonella isolates (each isolate represented one Salmonella serotype) pooled in one R9.4.1 flow cell. Each multiplexing strategy was repeated with five flow cells, and the loaded samples were sequenced simultaneously in a GridION sequencer for 48 h. In silico serotype prediction was performed using both SeqSero2 (for raw reads and genome assemblies) and SISTR (for genome assemblies) software suites. An average of 10.63 Gbp of clean sequencing data was obtained per flow cell. We found that the unevenness of data yield among each multiplexed isolate was a major barrier for shortening sequencing time. Using genome assemblies, both SeqSero2 and SISTR accurately predicted all the multiplexed isolates under each multiplexing strategy when depth of genome coverage ≥50× for each isolate. We identified that cross-sample barcode assignment was a major cause of prediction errors when raw sequencing data were used for prediction. This study also demonstrated that, (i) sequence data generated by ONT multiplex sequencing can be used to simultaneously predict serotype for three to ten Salmonella isolates, (ii) with three to ten Salmonella isolates multiplexed, genome coverage at ≥50× per isolate was obtained within an average of 6 h of ONT multiplex sequencing, and (iii) with five isolates multiplexed, the cost per isolate might be reduced to 23% of that incurred with single ONT sequencing. This study is a starting point for future validation of multiplex ONT WGS as a cost-efficient and rapid Salmonella confirmation and serotype classification tool for the food industry.

Validation strategy of a bioinformatics whole genome sequencing workflow for Shiga toxin-producing Escherichia coli using a reference collection extensively characterized with conventional methods

Whole genome sequencing (WGS) enables complete characterization of bacterial pathogenic isolates at single nucleotide resolution, making it the ultimate tool for routine surveillance and outbreak investigation. The lack of standardization, and the variation regarding bioinformatics workflows and parameters, however, complicates interoperability among (inter)national laboratories. We present a validation strategy applied to a bioinformatics workflow for Illumina data that performs complete characterization of Shiga toxin-producing Escherichia coli (STEC) isolates including antimicrobial resistance prediction, virulence gene detection, serotype prediction, plasmid replicon detection and sequence typing. The workflow supports three commonly used bioinformatics approaches for the detection of genes and alleles: alignment with blast+, kmer-based read mapping with KMA, and direct read mapping with SRST2. A collection of 131 STEC isolates collected from food and human sources, extensively characterized with conventional molecular methods, was used as a validation dataset. Using a validation strategy specifically adopted to WGS, we demonstrated high performance with repeatability, reproducibility, accuracy, precision, sensitivity and specificity above 95 % for the majority of all assays. The WGS workflow is publicly available as a 'push-button' pipeline at https://galaxy.sciensano.be. Our validation strategy and accompanying reference dataset consisting of both conventional and WGS data can be used for characterizing the performance of various bioinformatics workflows and assays, facilitating interoperability between laboratories with different WGS and bioinformatics set-ups.

Integrated Whole-Genome Sequencing Infrastructure for Outbreak Detection and Source Tracing of Salmonella enterica Serotype Enteritidis

As an important foodborne pathogen, Salmonella enterica serotype Enteritidis is recognized as one of the most common causes of human salmonellosis globally. Outbreak detection for this highly homogenous serotype, however, has remained challenging. Rapid advances in sequencing technologies have presented whole-genome sequencing (WGS) as a significant advancement for source tracing and molecular typing of foodborne pathogens. A retrospective analysis was conducted using Salmonella Enteritidis isolates (n = 65) from 11 epidemiologically confirmed outbreaks and a collection of contemporaneous sporadic isolates (n = 258) during 2007-2017 to evaluate the performance of WGS in delineating outbreak-associated isolates. Whole-genome single-nucleotide polymorphism (SNP)-based phylogenetic analysis revealed well-supported clades in concordance with epidemiological evidence and pairwise distances of ≤3 SNPs for all outbreaks. WGS-based framework of outbreak detection was thus proposed and applied prospectively to investigate isolates (n = 66) from nine outbreaks during 2018-2019. We further demonstrated the superior discriminatory power and accuracy of WGS to resolve and delineate outbreaks for pragmatic food source tracing. The proposed integrated WGS framework is the first in China for Salmonella Enteritidis and has the potential to serve as a paradigm for outbreak detection and source tracing of Salmonella throughout the stages of food production, as well as expanded to other foodborne pathogens.

Laboratory response checklist for infectious disease outbreaks-preparedness and response considerations for emerging threats

The purpose of the Laboratory Response Checklist for Infectious Disease Outbreaks (the Checklist) is to provide public health laboratories and laboratory networks operating at multiple jurisdictional levels with a useful, adaptable tool to help rapidly identify important outbreak response considerations, particularly when investigating a previously unknown infectious disease threat. The Checklist was developed by the National Microbiology Laboratory of Canada in collaboration with provincial/territorial, national and international laboratory experts, including the Canadian Public Health Laboratory Network, and the Global Health Security Action Group Laboratory Network. While the Checklist was initially designed to reflect lessons learned through National Microbiology Laboratory participation in extended national and international outbreak responses (e.g. Zika virus epidemic [2015-2016], Ebola virus epidemic, West Africa [2014-2016]), the importance of optimizing laboratory response coordination has only been underscored by the ongoing challenges presented by the coronavirus disease 2019 (COVID-19) pandemic response requirements. The Checklist identifies five highly interdependent laboratory response themes, each of which encompasses multiple considerations that may be critical to a coordinated, strategic outbreak response. As such, the comprehensive review of Checklist considerations by responding laboratory organizations may provide a valuable opportunity to quickly detect key response considerations and interdependencies, and mitigate risks with the potential to impact public health action.

Prevalence and risk factors of Salmonella in commercial poultry farms in Nigeria

Salmonella is an important human pathogen and poultry products constitute an important source of human infections. This study investigated prevalence; identified serotypes based on whole genome sequence, described spatial distribution of Salmonella serotypes and predicted risk factors that could influence the prevalence of Salmonella infection in commercial poultry farms in Nigeria. A cross sectional approach was employed to collect 558 pooled shoe socks and dust samples from 165 commercial poultry farms in North West Nigeria. On-farm visitation questionnaires were administered to obtain information on farm management practices in order to assess risk factors for Salmonella prevalence. Salmonella was identified by culture, biotyping, serology and polymerase chain reaction (PCR). PCR confirmed isolates were paired-end Illumina- sequenced. Following de novo genome assembly, draft genomes were used to obtain serotypes by SeqSero2 and SISTR pipeline and sequence types by SISTR and Enterobase. Risk factor analysis was performed using the logit model. A farm prevalence of 47.9% (CI95 [40.3-55.5]) for Salmonella was observed, with a sample level prevalence of 15.9% (CI95 [12.9-18.9]). Twenty-three different serotypes were identified, with S. Kentucky and S. Isangi as the most prevalent (32.9% and 11%). Serotypes showed some geographic variation. Salmonella detection was strongly associated with disposal of poultry waste and with presence of other livestock on the farm. Salmonella was commonly detected on commercial poultry farms in North West Nigeria and S. Kentucky was found to be ubiquitous in the farms.

Analytical Performance Validation of Next-Generation Sequencing Based Clinical Microbiology Assays Using a K-mer Analysis Workflow

Next-generation sequencing (NGS) enables clinical microbiology assays such as molecular typing of bacterial isolates which is now routinely applied for infection control and epidemiology. Additionally, feasibility for NGS-based identification of antimicrobial resistance (AMR) markers as well as genetic prediction of antibiotic susceptibility testing results has been demonstrated. Various bioinformatics approaches enabling NGS-based clinical microbiology assays exist, but standardized, computationally efficient and scalable sample-to-results workflows including validated quality control parameters are still lacking. Bioinformatics analysis workflows based on k-mers have been shown to allow for fast and efficient analysis of large genomics data sets as obtained from microbial sequencing applications. We here demonstrate applicability of k-mer based clinical microbiology assays for whole-genome sequencing (WGS) including variant calling, taxonomic identification, bacterial typing as well as AMR marker detection. The wet-lab and dry-lab workflows were developed and validated in line with Clinical Laboratory Improvement Act (CLIA) guidelines for laboratory-developed tests (LDTs) on multi-drug resistant ESKAPE pathogens. The developed k-mer based workflow demonstrated ≥99.39% repeatability, ≥99.09% reproducibility and ≥99.76% accuracy for variant calling and applied assays as determined by intra-day and inter-day triplicate measurements. The limit of detection (LOD) across assays was found to be at 20× sequencing depth and 15× for AMR marker detection. Thorough benchmarking of the k-mer based workflow revealed analytical performance criteria are comparable to state-of-the-art alignment based workflows across clinical microbiology assays. Diagnostic sensitivity and specificity for multilocus sequence typing (MLST) and phylogenetic analysis were 100% for both approaches. For AMR marker detection, sensitivity and specificity were 95.29 and 99.78% for the k-mer based workflow as compared to 95.17 and 99.77% for the alignment-based approach. Summarizing, results illustrate that k-mer based analysis workflows enable a broad range of clinical microbiology assays, potentially not only for WGS-based typing and AMR gene detection but also genetic prediction of antibiotic susceptibility testing results.

A genoserotyping system for a fast and objective identification of Salmonella serotypes commonly isolated from poultry and pork food sectors in Belgium

Humans are mostly contaminated by Salmonella through the consumption of pork- and poultry-derived food products. Therefore, a strict monitoring of Salmonella serotypes in food-producing animals is needed to limit the transmission of the pathogen to humans. Additionally, Salmonella can lead to economic loss in the food sector. Previously, a genoserotyping method using the MOL-PCR and Luminex technology was developed for the identification of the 6 Salmonella serotypes, and their variants, subjected to an official control in the Belgian food sector. In this study, 3 additional assays using the same technology were developed for the rapid and cost-effective detection of 13 dangerous highly invasive serotypes or other serotypes frequently isolated from the Belgian poultry and pork sector, i.e. Agona, Anatum, Brandenburg, Choleraesuis, Derby, Enteritidis vaccine strains, Gallinarum var. Gallinarum/Pullorum, Livingstone, Mbandaka, Minnesota, Ohio, Rissen and Senftenberg. Moreover, the previously developed first MOL-PCR assay was improved for S. Paratyphi B and serogroup O:3 detection. Finally, a Decision Support System hosted by a web application was created for an automatic and objective interpretation of the Luminex raw data. The 3 new assays and the modifications of the first assay were validated with a 100% accuracy, using 553 Salmonella and non-Salmonella strains in total.

Systematic Evaluation of Whole Genome Sequence-Based Predictions of Salmonella Serotype and Antimicrobial Resistance

Whole-genome sequencing (WGS) is used increasingly in public-health laboratories for typing and characterizing foodborne pathogens. To evaluate the performance of existing bioinformatic tools for in silico prediction of antimicrobial resistance (AMR) and serotypes of Salmonella enterica, WGS-based genotype predictions were compared with the results of traditional phenotyping assays. A total of 111 S. enterica isolates recovered from a Canadian baseline study on broiler chicken conducted in 2012-2013 were selected based on phenotypic resistance to 15 different antibiotics and isolates were subjected to WGS. Both SeqSero2 and SISTR accurately determined S. enterica serotypes, with full matches to laboratory results for 87.4 and 89.2% of isolates, respectively, and partial matches for the remaining isolates. Antimicrobial resistance genes (ARGs) were identified using several bioinformatics tools including the Comprehensive Antibiotic Resistance Database – Resistance Gene Identifier (CARD-RGI), Center for Genomic Epidemiology (CGE) ResFinder web tool, Short Read Sequence Typing for Bacterial Pathogens (SRST2 v 0.2.0), and k-mer alignment method (KMA v 1.17). All ARG identification tools had ≥ 99% accuracy for predicting resistance to all antibiotics tested except streptomycin (accuracy 94.6%). Evaluation of ARG detection in assembled versus raw-read WGS data found minimal observable differences that were gene- and coverage- dependent. Where initial phenotypic results indicated isolates were sensitive, yet ARGs were detected, repeat AMR testing corrected discrepancies. All tools failed to find resistance-determining genes for one gentamicin- and two streptomycin-resistant isolates. Further investigation found a single nucleotide polymorphism (SNP) in the nuoF coding region of one of the isolates which may be responsible for the observed streptomycin-resistant phenotype. Overall, WGS-based predictions of AMR and serotype were highly concordant with phenotype determination regardless of computational approach used.

Genome-based Salmonella serotyping as the new gold standard

Salmonella enterica is the second most reported bacterial cause of food-borne infections in Europe. Therefore molecular surveillance activities based on pathogen subtyping are an important measure of controlling Salmonellosis by public health agencies. In Germany, at the federal level, this work is carried out by the National Reference Center for Salmonella and other Bacterial Enteric Pathogens (NRC). With rise of next generation sequencing techniques, the NRC has introduced whole-genome-based typing methods for S. enterica in 2016. In this study we report on the feasibility of genome-based in silico serotyping in the German setting using raw sequence reads. We found that SeqSero and seven gene MLST showed 98% and 95% concordance, respectively, with classical serotyping for the here evaluated serotypes, including the most common German serotypes S. Enteritidis and S. Typhimurium as well as less frequently found serotypes. The level of concordance increased to >99% when the results of both in silico methods were combined. However, both tools exhibited misidentification of monophasic variants, in particular monophasic S. Typhimurium and therefore need to be fine-tuned for reliable detection of this epidemiologically important variant. We conclude that with adjustments Salmonella genome-based serotyping might become the new gold standard.

Performance and Accuracy of Four Open-Source Tools for In Silico Serotyping of Salmonella spp. Based on Whole-Genome Short-Read Sequencing Data

We compared the performance of four open-source in silico Salmonella typing tools (SeqSero, SeqSero2, Salmonella In Silico Typing Resource [SISTR], and Metric Oriented Sequence Typer [MOST]) to assess their potential for replacing laboratory serological testing with serovar predictions from whole-genome sequencing data. We conducted a retrospective analysis of 1,624 Salmonella isolates of 72 serovars submitted to the German National Salmonella Reference Laboratory between 1999 and 2019. All isolates are derived from animal and foodstuff origins. We conducted Illumina short-read sequencing and compared the in silico serovar prediction results with the results of routine laboratory serotyping. We found the best-performing in silico serovar prediction tool to be SISTR, with 94% correctly typed isolates, followed by SeqSero2 (87%), SeqSero (81%), and MOST (79%). Furthermore, we found that mapping-based tools like SeqSero and SeqSero2 (allele mode) were more reliable for the prediction of monophasic variants, while sequence type and cluster-based methods like MOST and SISTR (core-genome multilocus sequence type [cgMLST]), showed greater resilience when confronted with GC-biased sequencing data. We showed that the choice of library preparation kit could substantially affect O antigen detection, due to the low GC content of the wzx and wzy genes. Although the accuracy of computational serovar predictions is still not quite on par with traditional serotyping by Salmonella reference laboratories, the command-line tools investigated in this study perform a rapid, efficient, inexpensive, and reproducible analysis, which can be integrated into in-house characterization pipelines. Based on our results, we find SISTR most suitable for automated, routine serotyping for public health surveillance of Salmonella IMPORTANCE Salmonella spp. are important foodborne pathogens. To reduce the number of infected patients, it is essential to understand which subtypes of the bacteria cause disease outbreaks. Traditionally, characterization of Salmonella requires serological testing, a laboratory method by which Salmonella isolates can be classified into over 2,600 distinct subtypes, called serovars. Due to recent advances in whole-genome sequencing, many tools have been developed to replace traditional testing methods with computational analysis of genome sequences. It is crucial to validate that these tools, many already in use for routine surveillance, deliver accurate and reliable serovar information. In this study, we set out to compare which of the currently available open-source command-line tools is most suitable to replace serological testing. A thorough evaluation of the differing computational approaches is highly important to ensure the backward compatibility of serotyping data and to maintain comparability between laboratories.

Typing methods based on whole genome sequencing data

Whole genome sequencing (WGS) of foodborne pathogens has become an effective method for investigating the information contained in the genome sequence of bacterial pathogens. In addition, its highly discriminative power enables the comparison of genetic relatedness between bacteria even on a sub-species level. For this reason, WGS is being implemented worldwide and across sectors (human, veterinary, food, and environment) for the investigation of disease outbreaks, source attribution, and improved risk characterization models. In order to extract relevant information from the large quantity and complex data produced by WGS, a host of bioinformatics tools has been developed, allowing users to analyze and interpret sequencing data, starting from simple gene-searches to complex phylogenetic studies. Depending on the research question, the complexity of the dataset and their bioinformatics skill set, users can choose between a great variety of tools for the analysis of WGS data. In this review, we describe the relevant approaches for phylogenomic studies for outbreak studies and give an overview of selected tools for the characterization of foodborne pathogens based on WGS data. Despite the efforts of the last years, harmonization and standardization of typing tools are still urgently needed to allow for an easy comparison of data between laboratories, moving towards a one health worldwide surveillance system for foodborne pathogens.

Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms

This Opinion considers the application of whole genome sequencing (WGS) and metagenomics for outbreak investigation, source attribution and risk assessment of food‐borne pathogens. WGS offers the highest level of bacterial strain discrimination for food‐borne outbreak investigation and source‐attribution as well as potential for more precise hazard identification, thereby facilitating more targeted risk assessment and risk management. WGS improves linking of sporadic cases associated with different food products and geographical regions to a point source outbreak and can facilitate epidemiological investigations, allowing also the use of previously sequenced genomes. Source attribution may be favoured by improved identification of transmission pathways, through the integration of spatial‐temporal factors and the detection of multidirectional transmission and pathogen–host interactions. Metagenomics has potential, especially in relation to the detection and characterisation of non‐culturable, difficult‐to‐culture or slow‐growing microorganisms, for tracking of hazard‐related genetic determinants and the dynamic evaluation of the composition and functionality of complex microbial communities. A SWOT analysis is provided on the use of WGS and metagenomics for Salmonella and Shigatoxin‐producing Escherichia coli (STEC) serotyping and the identification of antimicrobial resistance determinants in bacteria. Close agreement between phenotypic and WGS‐based genotyping data has been observed. WGS provides additional information on the nature and localisation of antimicrobial resistance determinants and on their dissemination potential by horizontal gene transfer, as well as on genes relating to virulence and biological fitness. Interoperable data will play a major role in the future use of WGS and metagenomic data. Capacity building based on harmonised, quality controlled operational systems within European laboratories and worldwide is essential for the investigation of cross‐border outbreaks and for the development of international standardised risk assessments of food‐borne microorganisms.

Development of a multi-locus typing scheme for an Enterobacteriaceae linear plasmid that mediates inter-species transfer of flagella

Due to the public health importance of flagellar genes for typing, it is important to understand mechanisms that could alter their expression or presence. Phenotypic novelty in flagellar genes arise predominately through accumulation of mutations but horizontal transfer is known to occur. A linear plasmid termed pBSSB1 previously identified in Salmonella Typhi, was found to encode a flagellar operon that can mediate phase variation, which results in the rare z66 flagella phenotype. The identification and tracking of homologs of pBSSB1 is limited because it falls outside the normal replicon typing schemes for plasmids. Here we report the generation of nine new pBSSB1-family sequences using Illumina and Nanopore sequence data. Homologs of pBSSB1 were identified in 154 genomes representing 25 distinct serotypes from 67,758 Salmonella public genomes. Pangenome analysis of pBSSB1-family contigs was performed using roary and we identified three core genes amenable to a minimal pMLST scheme. Population structure analysis based on the newly developed pMLST scheme identified three major lineages representing 35 sequence types, and the distribution of these sequence types was found to span multiple serovars across the globe. This in silico pMLST scheme has shown utility in tracking and subtyping pBSSB1-family plasmids and it has been incorporated into the plasmid MLST database under the name “pBSSB1-family”.

SeqSero2: Rapid and Improved Salmonella Serotype Determination Using Whole-Genome Sequencing Data

SeqSero, launched in 2015, is a software tool for Salmonella serotype determination from whole-genome sequencing (WGS) data. Despite its routine use in public health and food safety laboratories in the United States and other countries, the original SeqSero pipeline is relatively slow (minutes per genome using sequencing reads), is not optimized for draft genome assemblies, and may assign multiple serotypes for a strain. Here, we present SeqSero2 (github.com/denglab/SeqSero2; denglab.info/SeqSero2), an algorithmic transformation and functional update of the original SeqSero. Major improvements include (i) additional sequence markers for identification of Salmonella species and subspecies and certain serotypes, (ii) a k-mer based algorithm for rapid serotype prediction from raw reads (seconds per genome) and improved serotype prediction from assemblies, and (iii) a targeted assembly approach for specific retrieval of serotype determinants from WGS for serotype prediction, new allele discovery, and prediction troubleshooting. Evaluated using 5,794 genomes representing 364 common U.S. serotypes, including 2,280 human isolates of 117 serotypes from the National Antimicrobial Resistance Monitoring System, SeqSero2 is up to 50 times faster than the original SeqSero while maintaining equivalent accuracy for raw reads and substantially improving accuracy for assemblies. SeqSero2 further suggested that 3% of the tested genomes contained reads from multiple serotypes, indicating a use for contamination detection. In addition to short reads, SeqSero2 demonstrated potential for accurate and rapid serotype prediction directly from long nanopore reads despite base call errors. Testing of 40 nanopore-sequenced genomes of 17 serotypes yielded a single H antigen misidentification.IMPORTANCE Serotyping is the basis of public health surveillance of Salmonella It remains a first-line subtyping method even as surveillance continues to be transformed by whole-genome sequencing. SeqSero allows the integration of Salmonella serotyping into a whole-genome-sequencing-based laboratory workflow while maintaining continuity with the classic serotyping scheme. SeqSero2, informed by extensive testing and application of SeqSero in the United States and other countries, incorporates important improvements and updates that further strengthen its application in routine and large-scale surveillance of Salmonella by whole-genome sequencing.

Salmonella Serotyping; Comparison of the Traditional Method to a Microarray-Based Method and an in silico Platform Using Whole Genome Sequencing Data

Salmonella is one of the most common causes of food-borne diseases worldwide. While Salmonella molecular subtyping by Whole Genome Sequencing (WGS) is increasingly used for outbreak and source tracking investigations, serotyping remains as a first-line characterization of Salmonella isolates. The traditional phenotypic method for serotyping is logistically challenging, as it requires the use of more than 150 specific antisera and well trained personnel to interpret the results. Consequently, it is not a routine method for the majority of laboratories. Several rapid molecular methods targeting O and H loci or surrogate genomic markers have been developed as alternative solutions. With the expansion of WGS, in silico Salmonella serotype prediction using WGS data is available. Here, we compared a microarray method using molecular markers, the Check and Trace Salmonella assay (CTS) and a WGS-based serotype prediction tool that targets molecular determinants of serotype (SeqSero) to the traditional phenotypic method using 100 strains representing 45 common and uncommon serotypes. Compared to the traditional method, the CTS assay correctly serotyped 97% of the strains, four strains gave a double serotype prediction. Among the inconclusive data, one strain was not predicted and two strains were incorrectly identified. SeqSero was evaluated with two versions (SeqSero 1 and the alpha test version of SeqSero 2). The correct antigenic formula was predicted by SeqSero 1 for 96 and 95% of strains using raw reads and assembly, respectively. However, 34 and 33% of these predictions included multiple serotypes by raw reads and assembly. With raw reads, one strain was not identified and three strains were discordant with phenotypic serotyping result. With assembly, three strains were not predicted and two strains were incorrectly predicted. While still under development, SeqSero 2 maintained the accuracy of antigenic formula prediction at 98% and reduced multiple serotype prediction rate to 13%. One strain had no prediction and one strain was incorrectly predicted. Our study indicates that the CTS assay is a good alternative for routine laboratories as it is an easy to use method with a short turn-around-time. SeqSero is a reliable replacement for phenotypic serotyping if WGS is routinely implemented.

Characterizing Salmonella enterica Serovar Choleraesuis, var. Kunzendorf: A Comparative Case Study

Different Salmonella serovars generally display different antigenic formulae, but there are some exceptions. For instance, the same antigenic formula, 6,7:c:1,5, is shared by Salmonella enterica serovar, Paratyphi C, Typhisuis, and Choleraesuis. Moreover, three biotypes have been described within the S. Choleraesuis serovar. A distinction among such biotypes can only be based on biochemical behaviors (biotyping) posing serious concerns when rapid characterization is required. The study of an outbreak of severe epizootic salmonellosis in wild boars occurred in Italy between 2012 and 2014 and the typing of the isolates recovered from the outbreak were used to test different approaches for serovar identification. A number of 30 S. Choleraesuis var. Kunzendorf isolates from the outbreak were typed by means of four different methods to derive serovar and biotype: (i) slide agglutination method followed by biochemical tests, (ii) suspension array xMAP® Salmonella Serotyping Assay (SSA), (iii) whole genome sequencing (WGS) and data analysis using SeqSero tool, and (iv) WGS and data analysis using Salmonella TypeFinder tool. Slide agglutination, xMAP® SSA and WGS, followed by SeqSero analysis, are methods that infer the serovars according to the White-Kauffmann-Le Minor (WKL) scheme, based exclusively on antigens. Using these methods, isolates with incomplete antigenic formulae could be misleadingly excluded from an outbreak. On the contrary, WGS followed by Salmonella TypeFinder data analysis, which predicts the serotype on the basis of Multilocus sequence typing (MLST), might be able to cluster together isolates belonging to the same outbreak irrespective of the antigenic formula. Results suggest the benefit of routine use of a combination of in silico MLST and antigenic formula analysis to solve specific ambiguous case studies for outbreak investigation purposes.

Where the plasmids roam: large-scale sequence analysis reveals plasmids with large host ranges

Describing the role of plasmids and their contribution to the exchange of genetic material among bacteria is essential for understanding the fields of plasmid epidemiology, microbial ecology, and commercial and synthetic microbiology. Broad-host-range (BHR) plasmids are those that are found not only in a single bacterial species, but in members of different taxonomic groups and are of significant interest to researchers in many fields. We applied a novel approach to computationally identify new BHR plasmids, in which we searched for highly similar cognate plasmids within a comprehensive plasmid database. After identifying 125 plasmid groups with highly similar cognates found in multiple taxa, we closely examined BHR plasmids found in multiple families. The majority of our identified BHR plasmids are found in members of the Enterobacteriaceae and closely related taxa, while three BHR plasmids of potential commercial significance were found in two species of Cyanobacteria. One plasmid with an exceptionally broad host range was found in both Gram-positive and Gram-negative bacterial species. This analysis demonstrates the utility of this method in identifying new BHR plasmids while highlighting unknown ranges of previously documented plasmids.

The ADP-Ribosylating Toxins of Salmonella

A number of pathogenic bacteria utilize toxins to mediate disease in a susceptible host. The foodborne pathogen Salmonella is one of the most important and well-studied bacterial pathogens. Recently, whole genome sequence characterizations revealed the presence of multiple novel ADP-ribosylating toxins encoded by a variety of Salmonella serovars. In this review, we discuss both the classical (SpvB) and novel (typhoid toxin, ArtAB, and SboC/SeoC) ADP-ribosylating toxins of Salmonella, including the structure and function of these toxins and our current understanding of their contributions to virulence.

Operational models and criteria for incorporating microbial whole genome sequencing in hospital microbiology - A systematic literature review

BACKGROUND

Microbial whole genome sequencing (WGS) has many advantages over standard microbiological methods. However, it is not yet widely implemented in routine hospital diagnostics due to notable challenges.

OBJECTIVES

The aim was to extract managerial, financial and clinical criteria supporting the decision to implement WGS in routine diagnostic microbiology, across different operational models of implementation in the hospital setting.

METHODS

This was a systematic review of literature identified through PubMed and Web of Science. English literature studies discussing the applications of microbial WGS without limitation on publication date were eligible. A narrative approach for categorization and synthesis of the sources identified was adopted.

RESULTS

A total of 98 sources were included. Four main alternative operational models for incorporating WGS in clinical microbiology laboratories were identified: full in-house sequencing and analysis, full outsourcing of sequencing and analysis and two hybrid models combining in-house/outsourcing of the sequencing and analysis components. Six main criteria (and multiple related sub-criteria) for WGS implementation emerged from our review and included cost (e.g. the availability of resources for capital and operational investment); manpower (e.g. the ability to provide training programmes or recruit trained personnel), laboratory infrastructure (e.g. the availability of supplies and consumables or sequencing platforms), bioinformatics requirements (e.g. the availability of valid analysis tools); computational infrastructure (e.g. the availability of storage space or data safety arrangements); and quality control (e.g. the existence of standardized procedures).

CONCLUSIONS

The decision to incorporate WGS in routine diagnostics involves multiple, sometimes competing, criteria and sub-criteria. Mapping these criteria systematically is an essential stage in developing policies for adoption of this technology, e.g. using a multicriteria decision tool. Future research that will prioritize criteria and sub-criteria that were identified in our review in the context of operational models will inform decision-making at clinical and managerial levels with respect to effective implementation of WGS for routine use. Beyond WGS, similar decision-making challenges are expected with respect to future integration of clinical metagenomics.

Phylogenomic Pipeline Validation for Foodborne Pathogen Disease Surveillance

Foodborne pathogen surveillance in the United States is transitioning from strain identification using restriction digest technology (pulsed-field gel electrophoresis [PFGE]) to shotgun sequencing of the entire genome (whole-genome sequencing [WGS]). WGS requires a new suite of analysis tools, some of which have long histories in academia but are new to the field of public health and regulatory decision making. Although the general workflow is fairly standard for collecting and analyzing WGS data for disease surveillance, there are a number of differences in how the data are collected and analyzed across public health agencies, both nationally and internationally. This impedes collaborative public health efforts, so national and international efforts are underway to enable direct comparison of these different analysis methods. Ultimately, the harmonization efforts will allow the (mutually trusted and understood) production and analysis of WGS data by labs and agencies worldwide, thus improving outbreak response capabilities globally. This review provides a historical perspective on the use of WGS for pathogen tracking and summarizes the efforts underway to ensure the major steps in phylogenomic pipelines used for pathogen disease surveillance can be readily validated. The tools for doing this will ensure that the results produced are sound, reproducible, and comparable across different analytic approaches.

In silico Identification of Serovar-Specific Genes for Salmonella Serotyping

Salmonella enterica subspecies enterica is a highly diverse subspecies with more than 1500 serovars and the ability to distinguish serovars within this group is vital for surveillance. With the development of whole-genome sequencing technology, serovar prediction by traditional serotyping is being replaced by molecular serotyping. Existing in silico serovar prediction approaches utilize surface antigen encoding genes, core genome MLST and serovar-specific gene markers or DNA fragments for serotyping. However, these serovar-specific gene markers or DNA fragments only distinguished a small number of serovars. In this study, we compared 2258 Salmonella accessory genomes to identify 414 candidate serovar-specific or lineage-specific gene markers for 106 serovars which includes 24 polyphyletic serovars and the paraphyletic serovar Enteritidis. A combination of several lineage-specific gene markers can be used for the clear identification of the polyphyletic serovars and the paraphyletic serovar. We designed and evaluated an in silico serovar prediction approach by screening 1089 genomes representing 106 serovars against a set of 131 serovar-specific gene markers. The presence or absence of one or more serovar-specific gene markers was used to predict the serovar of an isolate from genomic data. We show that serovar-specific gene markers have comparable accuracy to other in silico serotyping methods with 84.8% of isolates assigned to the correct serovar with no false positives (FP) and false negatives (FN) and 10.5% of isolates assigned to a small subset of serovars containing the correct serovar with varied FP. Combined, 95.3% of genomes were correctly assigned to a serovar. This approach would be useful as diagnosis moves to culture-independent and metagenomic methods as well as providing a third alternative to confirm other genome-based analyses. The identification of a set of gene markers may also be useful in the development of more cost-effective molecular assays designed to detect specific gene markers of the all major serovars in a region. These assays would be useful in serotyping isolates where cultures are no longer obtained and traditional serotyping is therefore impossible.

UltraStrain: An NGS-Based Ultra Sensitive Strain Typing Method for Salmonella enterica

In the last few years, advances in next-generation sequencing (NGS) technology for whole genome sequencing (WGS) of foodborne pathogens have provided drastic improvements in food pathogen outbreak surveillance. WGS of foodborne pathogen enables identification of pathogens from food or environmental samples, including difficult-to-detect pathogens in culture-negative infections. Compared to traditional low-resolution methods such as the pulsed-field gel electrophoresis (PFGE), WGS provides advantages to differentiate even closely related strains of the same species, thus enables rapid identification of food-source associated with pathogen outbreak events for a fast mitigation plan. In this paper, we present UltraStrain, which is a fast and ultra sensitive pathogen detection and strain typing method for Salmonella enterica (S. enterica) based on WGS data analysis. In the proposed method, a noise filtering step is first performed where the raw sequencing data are mapped to a synthetic species-specific reference genome generated from S. enterica specific marker sequences to avoid potential interference from closely related species for low spike samples. After that, a statistical learning based method is used to identify candidate strains, from a database of known S. enterica strains, that best explain the retained S. enterica specific reads.Finally, a refinement step is further performed by mapping all the reads before filtering onto the identified top candidate strains, and recalculating the probability of presence for each candidate strain. Experiment results using both synthetic and real sequencing data show that the proposed method is able to identify the correct S. enterica strains from low-spike samples, and outperforms several existing strain-typing methods in terms of sensitivity and accuracy.

Analysis of Salmonella enterica Serovar Typhi by Outer Membrane Protein (OMP) Profiling, Random Amplification of Polymorphic DNA (RAPD) and Pulsed Field Gel Electrophoresis (PFGE)

A number of countries, including developed countries, still have typhoid fever as a major problem resulting in frequent outbreaks. The importance of controlling spread of typhoid fever is well known and necessitates periodic studies to delineate epidemiological relationships. Although phage typing remains to be the preferred conventional method for characterisation of typhoid bacilli, it is of limited use due to prevalence of few predominant phage types in the country like India. Therefore, an effort has been made to assess three molecular methods [Outer Membrane Protein (OMP) Profiling, Random Amplification of Polymorphic DNA (RAPD) and Pulsed Field Gel Electrophoresis (PFGE)] for typing of Salmonella enterica serovar Typhi. 128 Salmonella enterica serovar Typhi isolates were identified using biotyping and serotyping followed by antimicrobial susceptibility testing. These isolates were further subjected to OMP analysis, RAPD and PFGE. PFGE (114 unique clusters) was found to be the most discriminatory method followed by RAPD (94 unique clusters) and OMP profiling (50 unique clusters). Multidrug resistant strains were well discriminated by all three methods used in the study. PFGE still remains the most preferred method for detailed epidemiological investigations. However, random amplification of polymorphic DNA and outer membrane protein profiling can also be considered for molecular discrimination of the isolates in the laboratories lacking high-end facilities.

Validation of a Bioinformatics Workflow for Routine Analysis of Whole-Genome Sequencing Data and Related Challenges for Pathogen Typing in a European National Reference Center: Neisseria meningitidis as a Proof-of-Concept

Despite being a well-established research method, the use of whole-genome sequencing (WGS) for routine molecular typing and pathogen characterization remains a substantial challenge due to the required bioinformatics resources and/or expertise. Moreover, many national reference laboratories and centers, as well as other laboratories working under a quality system, require extensive validation to demonstrate that employed methods are "fit-for-purpose" and provide high-quality results. A harmonized framework with guidelines for the validation of WGS workflows does currently, however, not exist yet, despite several recent case studies highlighting the urgent need thereof. We present a validation strategy focusing specifically on the exhaustive characterization of the bioinformatics analysis of a WGS workflow designed to replace conventionally employed molecular typing methods for microbial isolates in a representative small-scale laboratory, using the pathogen Neisseria meningitidis as a proof-of-concept. We adapted several classically employed performance metrics specifically toward three different bioinformatics assays: resistance gene characterization (based on the ARG-ANNOT, ResFinder, CARD, and NDARO databases), several commonly employed typing schemas (including, among others, core genome multilocus sequence typing), and serogroup determination. We analyzed a core validation dataset of 67 well-characterized samples typed by means of classical genotypic and/or phenotypic methods that were sequenced in-house, allowing to evaluate repeatability, reproducibility, accuracy, precision, sensitivity, and specificity of the different bioinformatics assays. We also analyzed an extended validation dataset composed of publicly available WGS data for 64 samples by comparing results of the different bioinformatics assays against results obtained from commonly used bioinformatics tools. We demonstrate high performance, with values for all performance metrics >87%, >97%, and >90% for the resistance gene characterization, sequence typing, and serogroup determination assays, respectively, for both validation datasets. Our WGS workflow has been made publicly available as a "push-button" pipeline for Illumina data at https://galaxy.sciensano.be to showcase its implementation for non-profit and/or academic usage. Our validation strategy can be adapted to other WGS workflows for other pathogens of interest and demonstrates the added value and feasibility of employing WGS with the aim of being integrated into routine use in an applied public health setting.

Random amplified polymorphic DNA-based molecular heterogeneity analysis of Salmonella enterica isolates from foods of animal origin

Aim:

This study aims to study the significance of random amplified polymorphic DNA (RAPD) typing in heterogeneity analysis of Salmonella serovars, isolated from foods of animal origin.

Materials and Methods:

Salmonella serovars isolated and identified from different foods of animal origin such as meat, milk, and egg by standard bacteriological methods. DNA isolated from all 10 isolates which are confirmed by biochemical and serotyping methods and then RAPD was performed using the primers OPB 10, primer 1290, NSC I, NSC II, and primer 3. Then, RAPD data were analyzed using the BioNumerics software, Belgium, Germany.

Results:

RAPD polymerase chain reaction (PCR) using five primers, namely OPB 10, primer 1290, NSC I, NSC II, and primer 3, classified the 10 isolates into 9, 10, 10, 7, and 10 RAPD-PCR types with discriminating powers of 0.1987, 0.423, 0.50889, 0.1842, and 0.2582, respectively. The phylogram constructed with NSC I profile classified isolates based on geographical origin. Primer 1290, NSC II, and primer 3 produced some uniform bands in all isolates indicating their binding ability in conserved genomic region. This study revealed that RAPD profile can be best used for finding out the heterogeneity at molecular level of Salmonella isolates in combination with other molecular and phenotypic typing techniques. Thus, our results support earlier observation of its significance by different workers on different Salmonella serotypes.

Conclusion:

Repeatability of RAPD-PCR is insufficient to distinguish genetic differences among Salmonella serovars.

Salmonella Serotyping Using Whole Genome Sequencing

Until recently, traditional serology and the Kauffmann White Scheme (KWS) have been the gold standard for Salmonella serotyping. Whole Genome Sequencing (WGS) has now emerged as an alternative in this field. Serotype information remains a cornerstone in food safety and public health activities to reduce the burden of salmonellosis. At the same time, recent advances in WGS have improved the ability to perform advanced pathogen characterization while improving trace back investigations to determine the source of foodborne illness during outbreaks. Serovar prediction based on WGS can be performed using in silico data analysis tools. Three such tools have been developed: (a). Salmonella in silico Typing Resource (SISTR), (b). SeqSero, and (c). in silico 7-gene MLST ST (Multilocus Sequence Typing Sub-Typing) which was generated using the SISTR platform. Public health officials around the world are diligently working to validate these tools for replacing traditional surveillance methods to provide a more powerful approach for molecular epidemiology in support of public health investigations. In this study, we report a retrospective analysis of our laboratory inventory of 1,041 Salmonella isolates collected between 1999 and 2017. These isolates are of public health significance since they all came from either food, feed or environmental swabs. They were all serotyped by both traditional serology and WGS using an in silico SeqSero tool for serovar prediction. Both predicted identical Salmonella serotypes in 899 isolates (86.4% of the 1,041 Salmonella isolates). SeqSero assignments differed from traditional serological testing in 80 isolates (7.7%) and no serotype prediction was ascertained from 62 isolates (5.9%). This retrospective study is an excellent example of using WGS and SeqSero as a data analysis tool to predict Salmonella serotypes that can provide numerous advantages including molecular and genetic details regarding the characteristics of the Salmonella isolates compared to traditional KWS serotyping. In conclusion, it is evident that using WGS and in silico tools for Salmonella serotyping might someday replace traditional serotyping.

Complete Genome and Plasmid Sequences of 32 Salmonella enterica Strains from 30 Serovars

We report here 32 completed closed genome sequences of strains representing 30 serotypes of Salmonella. These genome sequences will provide useful references for understanding the genetic variation within Salmonella enterica serotypes, particularly as references to aid in comparative genomics studies, as well as providing information for improving in silico serotyping accuracy.

Discrimination of non-typhoid Salmonella serogroups and serotypes by Fourier Transform Infrared Spectroscopy: A comprehensive analysis

Simpler, quick and low-cost methods for routine Salmonella enterica typing are required for epidemiologic surveillance of this important zoonotic pathogen. In this study, using a comprehensive isolate collection, we investigated the potential of Fourier transform infrared spectroscopy (FTIRS) to discriminate the most clinically-relevant serogroups and serotypes of non-typhoid Salmonella. Moreover, the role of O-units composition on the FTIRS Salmonella discrimination was also explored. S. enterica isolates (n = 325; 2002-2015; different sources and countries), of 57 serotypes and 15 serogroups [including the most frequent ones, B-n = 122; C-n = 108; D-n = 43 and E-n = 33)] were analysed by FTIRS. Infrared spectra were analysed by Partial Least Square Discriminant Analysis (PLSDA) and/or Principal Component Analysis (PCA). The polysaccharides region provided the spectral sharpest differences being used in the subsequent Salmonella typing. Serogroups (B, C, D and E) discrimination was achieved with high accuracy (99.6% of correct assignments; PLSDA model). Differences in the O-unit structures composition of those serogroups are likely justifying the discrimination achieved. Other serogroups (G, H, K, L, M, N, O, T, U, Y, Z) were correctly predicted as not belonging to serogroups B, C, D nor E, except for 3 isolates of serogroups H (S. Sundsvall, n = 1) and K (S. Cerro, n = 2). In fact, O-unit structure of serogroup H and K shows some similarity with sub-serogroup C1 with the remaining serogroups presenting marked differences in this cellular component. The sub-serogroups discrimination was successfully achieved for C1, C2 and C3 (using PCA), and for E1-E2-E3 and E4 (by PLSDA). Appropriate serotype discrimination was obtained for most of S. Rissen from the remaining C1 serotypes (91.5%-PLSDA), and S. Enteritidis (D1) from the remaining D1/D2 serotypes (93.4%-PLSDA). The lack of available O-unit composition for particular serotypes prevents the elucidation of the role of this cellular component on the discrimination at serotype level obtained. FTIRS was able to discriminate relevant serogroups (B, C, D and E), sub-serogroups (C1, C2 and C3; E1-E2-E3 and E4) and particular important serotypes (S. Enteritidis, S. Rissen and S. Senftenberg). Further studies on O-antigen composition would clarify the fundaments of discrimination obtained by FTIRS.

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.

GenomeTrakr proficiency testing for foodborne pathogen surveillance: an exercise from 2015

Pathogen monitoring is becoming more precise as sequencing technologies become more affordable and accessible worldwide. This transition is especially apparent in the field of food safety, which has demonstrated how whole-genome sequencing (WGS) can be used on a global scale to protect public health. GenomeTrakr coordinates the WGS performed by public-health agencies and other partners by providing a public database with real-time cluster analysis for foodborne pathogen surveillance. Because WGS is being used to support enforcement decisions, it is essential to have confidence in the quality of the data being used and the downstream data analyses that guide these decisions. Routine proficiency tests, such as the one described here, have an important role in ensuring the validity of both data and procedures. In 2015, the GenomeTrakr proficiency test distributed eight isolates of common foodborne pathogens to participating laboratories, who were required to follow a specific protocol for performing WGS. Resulting sequence data were evaluated for several metrics, including proper labelling, sequence quality and new single nucleotide polymorphisms (SNPs). Illumina MiSeq sequence data collected for the same set of strains across 21 different laboratories exhibited high reproducibility, while revealing a narrow range of technical and biological variance. The numbers of SNPs reported for sequencing runs of the same isolates across multiple laboratories support the robustness of our cluster analysis pipeline in that each individual isolate cultured and resequenced multiple times in multiple places are all easily identifiable as originating from the same source.