Comprehensive assessment of the quality of Salmonella whole genome sequence data available in public sequence databases using the Salmonella in silico Typing Resource (SISTR)

The advantage of intergenic regions as genomic features for machine-learning-based host attribution of Salmonella Typhimurium from the USA

Salmonella enterica is a taxonomically diverse pathogen with over 2600 serovars associated with a wide variety of animal hosts including humans, other mammals, birds and reptiles. Some serovars are host-specific or host-restricted and cause disease in distinct host species, while others, such as serovar S. Typhimurium (STm), are generalists and have the potential to colonize a wide variety of species. However, even within generalist serovars such as STm it is becoming clear that pathovariants exist that differ in tropism and virulence. Identifying the genetic factors underlying host specificity is complex, but the availability of thousands of genome sequences and advances in machine learning have made it possible to build specific host prediction models to aid outbreak control and predict the human pathogenic potential of isolates from animals and other reservoirs. We have advanced this area by building host-association prediction models trained on a wide range of genomic features and compared them with predictions based on nearest-neighbour phylogeny. SNPs, protein variants (PVs), antimicrobial resistance (AMR) profiles and intergenic regions (IGRs) were extracted from 3883 high-quality STm assemblies collected from humans, swine, bovine and poultry in the USA, and used to construct Random Forest (RF) machine learning models. An additional 244 recent STm assemblies from farm animals were used as a test set for further validation. The models based on PVs and IGRs had the best performance in terms of predicting the host of origin of isolates and outperformed nearest-neighbour phylogenetic host prediction as well as models based on SNPs or AMR data. However, the models did not yield reliable predictions when tested with isolates that were phylogenetically distinct from the training set. The IGR and PV models were often able to differentiate human isolates in clusters where the majority of isolates were from a single animal source. Notably, IGRs were the feature with the best performance across multiple models which may be due to IGRs acting as both a representation of their flanking genes, equivalent to PVs, while also capturing genomic regulatory variation, such as altered promoter regions. The IGR and PV models predict that ~45 % of the human infections with STm in the USA originate from bovine, ~40 % from poultry and ~14.5 % from swine, although sequences of isolates from other sources were not used for training. In summary, the research demonstrates a significant gain in accuracy for models with IGRs and PVs as features compared to SNP-based and core genome phylogeny predictions when applied within the existing population structure. This article contains data hosted by Microreact.

Phenotypic and genotypic survey of antibiotic resistance in Salmonella enterica isolates from dairy farms in Uruguay

Salmonella enterica is an important zoonotic pathogen that is frequently identified in dairy farming systems. An increase in antibiotic resistance has led to inadequate results of treatments, with impacts on animal and human health. Here, the phenotypic and genotypic susceptibility patterns of Salmonella isolates from dairy cattle and dairy farm environments were evaluated and compared. A collection of 75 S. enterica isolates were evaluated, and their phenotypic susceptibility was determined. For genotypic characterization, the whole genomes of the isolates were sequenced, and geno-serotypes, sequence types (STs) and core-genome-sequence types were determined using the EnteroBase pipeline. To characterize antibiotic resistance genes and gene mutations, tools from the Center for Genomic Epidemiology were used. Salmonella Dublin (SDu), S. Typhimurium (STy), S. Anatum (SAn), S. Newport (SNe), S. Agona (Sag), S. Montevideo (SMo) and IIIb 61:i:z53 were included in the collection. A single sequence type was detected per serovar. Phenotypic non-susceptibility to streptomycin and tetracycline was very frequent in the collection, and high non-susceptibility to ciprofloxacin was also observed. Multidrug resistance (MDR) was observed in 42 isolates (56.0%), with SAn and STy presenting higher MDR than the other serovars, showing non-susceptibility to up to 6 groups of antibiotics. Genomic analysis revealed the presence of 21 genes associated with antimicrobial resistance (AMR) in Salmonella isolates. More than 60% of the isolates carried some gene associated with resistance to aminoglycosides and tetracyclines. Only one gene associated with beta-lactam resistance was found, in seven isolates. Two different mutations were identified, parC_T57S and acrB_R717Q, which confer resistance to quinolones and azithromycin, respectively. The accuracy of predicting antimicrobial resistance phenotypes based on AMR genotypes was 83.7%. The genomic approach does not replace the phenotypic assay but offers valuable information for the survey of circulating antimicrobial resistance. This work represents one of the first studies evaluating phenotypic and genotypic AMR in Salmonella from dairy cattle in South America.

Genomic Epidemiology and Multilevel Genome Typing of Australian Salmonella enterica Serovar Enteritidis

Salmonella enterica serovar Enteritidis is one of the leading causes of salmonellosis in Australia. In this study, a total of 568 S. Enteritidis isolates from two Australian states across two consecutive years were analyzed and compared to international strains, using the S. Enteritidis multilevel genome typing (MGT) database, which contained 40,390 publicly available genomes from 99 countries. The Australian S. Enteritidis isolates were divided into three phylogenetic clades (A, B, and C). Clades A and C represented 16.4% and 3.5% of the total isolates, respectively, and were of local origin. Clade B accounted for 80.1% of the isolates which belonged to seven previously defined lineages but was dominated by the global epidemic lineage. At the MGT5 level, three out of five top sequence types (STs) in Australia were also top STs in Asia, suggesting that a fair proportion of Australian S. Enteritidis cases may be epidemiologically linked with Asian strains. In 2018, a large egg-associated local outbreak was caused by a recently defined clade B lineage prevalent in Europe and was closely related, but not directly linked, to three European isolates. Additionally, over half (54.8%) of predicted multidrug resistance (MDR) isolates belonged to 10 MDR-associated MGT-STs, which were also frequent in Asian S. Enteritidis . Overall, this study investigated the genomic epidemiology of S. Enteritidis in Australia, including the first large local outbreak, using MGT. The open MGT platform enables a standardized and sharable nomenclature that can be effectively applied to public health for unified surveillance of S. Enteritidis nationally and globally. IMPORTANCE Salmonella enterica serovar Enteritidis is a leading cause of foodborne infections. We previously developed a genomic typing database (MGTdb) for S. Enteritidis to facilitate global surveillance of this pathogen. In this study, we examined the genomic features of Australian S. Enteritidis using the MGTdb and found that Australian S. Enteritidis is mainly epidemiologically linked with Asian strains (especially strains carrying antimicrobial resistance genes), followed by European strains. The first large-scale egg-associated local outbreak in Australia was caused by a recently defined lineage prevalent in Europe, and three European isolates in the MGTdb were closely related but not directly linked to this outbreak. In summary, the S. Enteritidis MGTdb open platform is shown to be a potentially powerful tool for national and global public health surveillance of this pathogen.

Large-scale comparative genomics to refine the organization of the global Salmonella enterica population structure

The White-Kauffmann-Le Minor (WKL) scheme is the most widely used Salmonella typing scheme for reporting the disease prevalence of the enteric pathogen. With the advent of whole-genome sequencing (WGS), in silico methods have increasingly replaced traditional serotyping due to reproducibility, speed and coverage. However, despite integrating genomic-based typing by in silico serotyping tools such as SISTR, in silico serotyping in certain contexts remains ambiguous and insufficiently informative. Specifically, in silico serotyping does not attempt to resolve polyphyly. Furthermore, in spite of the widespread acknowledgement of polyphyly from genomic studies, the prevalence of polyphyletic serovars is not well characterized. Here, we applied a genomics approach to acquire the necessary resolution to classify genetically discordant serovars and propose an alternative typing scheme that consistently reflect natural Salmonella populations. By accessing the unprecedented volume of bacterial genomic data publicly available in GenomeTrakr and PubMLST databases (>180 000 genomes representing 723 serovars), we characterized the global Salmonella population structure and systematically identified putative non-monophyletic serovars. The proportion of putative non-monophyletic serovars was estimated higher than previous reports, reinforcing the inability of antigenic determinants to depict the complexity of Salmonella evolutionary history. We explored the extent of genetic diversity masked by serotyping labels and found significant intra-serovar molecular differences across many clinically important serovars. To avoid false discovery due to incorrect in silico serotyping calls, we cross-referenced reported serovar labels and concluded a low error rate in in silico serotyping. The combined application of clustering statistics and genome-wide association methods demonstrated effective characterization of stable bacterial populations and explained functional differences. The collective methods adopted in our study have practical values in establishing genomic-based typing nomenclatures for an entire microbial species or closely related subpopulations. Ultimately, we foresee an improved typing scheme to be a hybrid that integrates both genomic and antigenic information such that the resolution from WGS is leveraged to improve the precision of subpopulation classification while preserving the common names defined by the WKL scheme.

Serotyping, MLST, and Core Genome MLST Analysis of Salmonella enterica From Different Sources in China During 2004-2019

Salmonella enterica (S. enterica) is an important foodborne pathogen, causing food poisoning and human infection, and critically threatening food safety and public health. Salmonella typing is essential for bacterial identification, tracing, epidemiological investigation, and monitoring. Serotyping and multilocus sequence typing (MLST) analysis are standard bacterial typing methods despite the low resolution. Core genome MLST (cgMLST) is a high-resolution molecular typing method based on whole genomic sequencing for accurate bacterial tracing. We investigated 250 S. enterica isolates from poultry, livestock, food, and human sources in nine provinces of China from 2004 to 2019 using serotyping, MLST, and cgMLST analysis. All S. enterica isolates were divided into 36 serovars using slide agglutination. The major serovars in order were Enteritidis (31 isolates), Typhimurium (29 isolates), Mbandaka (23 isolates), and Indiana (22 isolates). All strains were assigned into 43 sequence types (STs) by MLST. Among them, ST11 (31 isolates) was the primary ST. Besides this, a novel ST, ST8016, was identified, and it was different from ST40 by position 317 C → T in dnaN. Furthermore, these 250 isolates were grouped into 185 cgMLST sequence types (cgSTs) by cgMLST. The major cgST was cgST235530 (11 isolates), and only three cgSTs contained isolates from human and other sources, indicating a possibility of cross-species infection. Phylogenetic analysis indicated that most of the same serovar strains were putatively homologous except Saintpaul and Derby due to their multilineage characteristics. In addition, serovar I 4,[5],12:i:- and Typhimurium isolates have similar genomic relatedness on the phylogenetic tree. In conclusion, we sorted out the phenotyping and genotyping diversity of S. enterica isolates in China during 2004-2019 and clarified the temporal and spatial distribution characteristics of Salmonella from different hosts in China in the recent 16 years. These results greatly supplement Salmonella strain resources, genetic information, and traceability typing data; facilitate the typing, traceability, identification, and genetic evolution analysis of Salmonella; and therefore, improve the level of analysis, monitoring, and controlling of foodborne microorganisms in China.

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.

Virulence genes identification and characterization revealed the presence of the Yersinia High Pathogenicity Island (HPI) in Salmonella from Brazil

Salmonella spp. is one of the major agents of foodborne disease worldwide, and its virulence genes are responsible for the main pathogenic mechanisms of this micro-organism. The whole-genome sequencing (WGS) of pathogens has become a lower-cost and more accessible genotyping tool providing many gene analysis possibilities. This study provided an in silico investigation of 129 virulence genes, including plasmidial and bacteriophage genes from Brazilian strains' public Salmonella genomes. The frequency analysis of the four most sequenced serovars and a temporal analysis over the past four decades was also performed. The NCBI sequence reads archive (SRA) database comprised 1077 Salmonella public whole-genome sequences of strains isolated in Brazil between 1968 and 2018. Among the 1077 genomes, 775 passed in Salmonella in silico Typing (SISTR) quality control, which also identified 41 different serovars in which the four most prevalent were S. Enteritidis, S. Typhimurium, S. Dublin, and S. Heidelberg. Among these, S. Heidelberg presented the most distinct virulence profile, besides presenting Yersinia High Pathogenicity Island (HPI), rare and first reported in Salmonella from Brazil. The genes mgtC, csgC, ssaI and ssaS were the most prevalent within the 775 genomes with more than 99% prevalence. On the other hand, the less frequent genes were astA, iucBCD, tptC and shdA, with less than 1% frequency. All of the plasmids and bacteriophages virulence genes presented a decreasing trend between the 2000 s and 2010 s decades, except for the phage gene grvA, which increased in this period. This study provides insights into Salmonella virulence genes distribution in Brazil using freely available bioinformatics tools. This approach could guide in vivo and in vitro studies besides being an interesting method for the investigation and surveillance of Salmonella virulence. Moreover, here we propose the genes mgtC, csgC, ssaI and ssaS as additional targets for PCR identification of Salmonella in Brazil due to their very high frequency in the studied genomes.

Salmonella nomenclature in the genomic era: a time for change

Salmonella enterica nomenclature has evolved over the past one hundred years into a highly sophisticated naming convention based on the recognition of antigens by specific antibodies. This serotyping scheme has led to the definition of over 2500 serovars which are well understood, have standing in nomenclature and, for the majority, biological relevance. Therefore, it is highly desirable for any change in naming convention to maintain backwards compatibility with the information linked to these serovars. The routine use of whole genome sequencing and the well-established link between sequence types and serovars presents an opportunity to update the scheme by incorporating the phylogenetically relevant sequence data whilst preserving the best of serotyping nomenclature. Advantages include: overcoming the variability in antibody preparations; removing the need to use laboratory animals and implementing a truly universal system. However, the issue of trying to reproduce the phenotyping gold standard needs to be relaxed if we are to fully embrace the genomic era. We have used whole genome sequence data from over 46,000 isolates of Salmonella enterica subspecies enterica to define clusters in two stages: Multi Locus Sequence Typing followed by antigen prediction. Sequence type—serotype discrepancies were resolved using core SNP clustering to determine the phylogenetic groups and this was confirmed by overlaying the antigenic prediction onto the core SNP clusters and testing the separation of clusters using cgMLST Hierarchical Clustering. This allowed us to define any major antigenic clusters within an ST—here called the MAC type and written as ST-serovar. Using this method, 99.96% of Salmonella isolates reported in the UK were assigned a MAC type and linked to a serovar name taken from the Kauffmann and White scheme. We propose a change for reporting of Salmonella enterica sub-types using the ST followed by serovar.

Genomic diversity of Salmonella enterica -The UoWUCC 10K genomes project

Background: Most publicly available genomes of Salmonella enterica are from human disease in the US and the UK, or from domesticated animals in the US. Methods: Here we describe a historical collection of 10,000 strains isolated between 1891-2010 in 73 different countries. They encompass a broad range of sources, ranging from rivers through reptiles to the diversity of all S. enterica isolated on the island of Ireland between 2000 and 2005. Genomic DNA was isolated, and sequenced by Illumina short read sequencing. Results: The short reads are publicly available in the Short Reads Archive. They were also uploaded to EnteroBase, which assembled and annotated draft genomes. 9769 draft genomes which passed quality control were genotyped with multiple levels of multilocus sequence typing, and used to predict serovars. Genomes were assigned to hierarchical clusters on the basis of numbers of pair-wise allelic differences in core genes, which were mapped to genetic Lineages within phylogenetic trees. Conclusions: The University of Warwick/University College Cork (UoWUCC) project greatly extends the geographic sources, dates and core genomic diversity of publicly available S. enterica genomes. We illustrate these features by an overview of core genomic Lineages within 33,000 publicly available Salmonella genomes whose strains were isolated before 2011. We also present detailed examinations of HC400, HC900 and HC2000 hierarchical clusters within exemplar Lineages, including serovars Typhimurium, Enteritidis and Mbandaka. These analyses confirm the polyphyletic nature of multiple serovars while showing that discrete clusters with geographical specificity can be reliably recognized by hierarchical clustering approaches. The results also demonstrate that the genomes sequenced here provide an important counterbalance to the sampling bias which is so dominant in current genomic sequencing.

Multilevel genome typing: genomics-guided scalable resolution typing of microbial pathogens

Background

Both long- and short-term epidemiology are fundamental to disease control and require accurate bacterial typing. Genomic data resulting from implementation of whole genome sequencing in many public health laboratories can potentially provide highly sensitive and accurate descriptions of strain relatedness. Previous typing efforts using these data have mainly focussed on outbreak detection.

Aim

We aimed to develop multilevel genome typing (MGT), using consecutive multilocus sequence typing (MLST) schemes of increasing sizes, stepping up from seven-gene MLST to core genome MLST, to allow examination of genetic relatedness at multiple resolution levels.

Methods

The system was applied to Salmonellaenterica serovar Typhimurium. The MLST scheme used at each step (MGT level), defined a given MGT-level specific sequence type (ST). The list of STs generated from all of these increasing MGT levels, was named a genome type (GT). Using MGT, we typed 9,096 previously characterised isolates with publicly available data.

Results

Our approach could identify previously described S. Typhimurium populations, such as the DT104 multidrug resistance lineage (GT 19-2-11) and two invasive lineages of African isolates (GT 313-2-3 and 313-2-752). Further, we showed that MGT-derived clusters can accurately distinguish five outbreaks from each other and five background isolates.

Conclusion

MGT provides a universal and stable nomenclature at multiple resolutions for S. Typhimurium strains and could be implemented as an internationally standardised strain identification system. While established so far only for S. Typhimurium, the results here suggest that MGT could form the basis for typing systems in other similar microorganisms.

Genomic diversity of Salmonella enterica -The UoWUCC 10K genomes project

Background: Most publicly available genomes of Salmonella enterica are from human disease in the US and the UK, or from domesticated animals in the US. Methods: Here we describe a historical collection of 10,000 strains isolated between 1891-2010 in 73 different countries. They encompass a broad range of sources, ranging from rivers through reptiles to the diversity of all S. enterica isolated on the island of Ireland between 2000 and 2005. Genomic DNA was isolated, and sequenced by Illumina short read sequencing. Results: The short reads are publicly available in the Short Reads Archive. They were also uploaded to EnteroBase, which assembled and annotated draft genomes. 9769 draft genomes which passed quality control were genotyped with multiple levels of multilocus sequence typing, and used to predict serovars. Genomes were assigned to hierarchical clusters on the basis of numbers of pair-wise allelic differences in core genes, which were mapped to genetic Lineages within phylogenetic trees. Conclusions: The University of Warwick/University College Cork (UoWUCC) project greatly extends the geographic sources, dates and core genomic diversity of publicly available S. enterica genomes. We illustrate these features by an overview of core genomic Lineages within 33,000 publicly available Salmonella genomes whose strains were isolated before 2011. We also present detailed examinations of HC400, HC900 and HC2000 hierarchical clusters within exemplar Lineages, including serovars Typhimurium, Enteritidis and Mbandaka. These analyses confirm the polyphyletic nature of multiple serovars while showing that discrete clusters with geographical specificity can be reliably recognized by hierarchical clustering approaches. The results also demonstrate that the genomes sequenced here provide an important counterbalance to the sampling bias which is so dominant in current genomic sequencing.

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.

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.

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.

The EnteroBase user's guide, with case studies on Salmonella transmissions, Yersinia pestis phylogeny, and Escherichia core genomic diversity

EnteroBase is an integrated software environment that supports the identification of global population structures within several bacterial genera that include pathogens. Here, we provide an overview of how EnteroBase works, what it can do, and its future prospects. EnteroBase has currently assembled more than 300,000 genomes from Illumina short reads from Salmonella, Escherichia, Yersinia, Clostridioides, Helicobacter, Vibrio, and Moraxella and genotyped those assemblies by core genome multilocus sequence typing (cgMLST). Hierarchical clustering of cgMLST sequence types allows mapping a new bacterial strain to predefined population structures at multiple levels of resolution within a few hours after uploading its short reads. Case Study 1 illustrates this process for local transmissions of Salmonella enterica serovar Agama between neighboring social groups of badgers and humans. EnteroBase also supports single nucleotide polymorphism (SNP) calls from both genomic assemblies and after extraction from metagenomic sequences, as illustrated by Case Study 2 which summarizes the microevolution of Yersinia pestis over the last 5000 years of pandemic plague. EnteroBase can also provide a global overview of the genomic diversity within an entire genus, as illustrated by Case Study 3, which presents a novel, global overview of the population structure of all of the species, subspecies, and clades within Escherichia.

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”.

Assessment and Comparison of Molecular Subtyping and Characterization Methods for Salmonella

The food industry is facing a major transition regarding methods for confirmation, characterization, and subtyping of Salmonella. Whole-genome sequencing (WGS) is rapidly becoming both the method of choice and the gold standard for Salmonella subtyping; however, routine use of WGS by the food industry is often not feasible due to cost constraints or the need for rapid results. To facilitate selection of subtyping methods by the food industry, we present: (i) a comparison between classical serotyping and selected widely used molecular-based subtyping methods including pulsed-field gel electrophoresis, multilocus sequence typing, and WGS (including WGS-based serovar prediction) and (ii) a scoring system to evaluate and compare Salmonella subtyping assays. This literature-based assessment supports the superior discriminatory power of WGS for source tracking and root cause elimination in food safety incident; however, circumstances in which use of other subtyping methods may be warranted were also identified. This review provides practical guidance for the food industry and presents a starting point for further comparative evaluation of Salmonella characterization and subtyping methods.

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

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

ConFindr: rapid detection of intraspecies and cross-species contamination in bacterial whole-genome sequence data

Whole-genome sequencing (WGS) of bacterial pathogens is currently widely used to support public-health investigations. The ability to assess WGS data quality is critical to underpin the reliability of downstream analyses. Sequence contamination is a quality issue that could potentially impact WGS-based findings; however, existing tools do not readily identify contamination from closely-related organisms. To address this gap, we have developed a computational pipeline, ConFindr, for detection of intraspecies contamination. ConFindr determines the presence of contaminating sequences based on the identification of multiple alleles of core, single-copy, ribosomal-protein genes in raw sequencing reads. The performance of this tool was assessed using simulated and lab-generated Illumina short-read WGS data with varying levels of contamination (0-20% of reads) and varying genetic distance between the designated target and contaminant strains. Intraspecies and cross-species contamination was reliably detected in datasets containing 5% or more reads from a second, unrelated strain. ConFindr detected intraspecies contamination with higher sensitivity than existing tools, while also being able to automatically detect cross-species contamination with similar sensitivity. The implementation of ConFindr in quality-control pipelines will help to improve the reliability of WGS databases as well as the accuracy of downstream analyses. ConFindr is written in Python, and is freely available under the MIT License at github.com/OLC-Bioinformatics/ConFindr.

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.

An improved DNA array-based classification method for the identification of Salmonella serotypes shows high concordance between traditional and genotypic testing

Previously we developed and tested the Salmonella GenoSerotyping Array (SGSA), which utilized oligonucleotide probes for O- and H- antigen biomarkers to perform accurate molecular serotyping of 57 Salmonella serotypes. Here we describe the development and validation of the ISO 17025 accredited second version of the SGSA (SGSA v. 2) with reliable and unambiguous molecular serotyping results for 112 serotypes of Salmonella which were verified both in silico and in vitro. Improvements included an expansion of the probe sets along with a new classifier tool for prediction of individual antigens and overall serotype from the array probe intensity results. The array classifier and probe sequences were validated in silico to high concordance using 36,153 draft genomes of diverse Salmonella serotypes assembled from public repositories. We obtained correct and unambiguous serotype assignments for 31,924 (88.30%) of the tested samples and a further 3,916 (10.83%) had fully concordant antigen predictions but could not be assigned to a single serotype. The SGSA v. 2 can directly use bacterial colonies with a limit of detection of 860 CFU/mL or purified DNA template at a concentration of 1.0 x 10⁻¹ ng/μl. The SGSA v. 2 was also validated in the wet laboratory and certified using panel of 406 samples representing 185 different serotypes with correct antigen and serotype determinations for 60.89% of the panel and 18.31% correctly identified but an ambiguous overall serotype determination.

Serotype Diversity and Antimicrobial Resistance among Salmonella enterica Isolates from Patients at an Equine Referral Hospital

Although Salmonella enterica can produce life-threatening colitis in horses, certain serotypes are more commonly associated with clinical disease. Our aim was to evaluate the proportional morbidity attributed to different serotypes, as well as the phenotypic and genotypic antimicrobial resistance (AMR) of Salmonella isolates from patients at an equine referral hospital in the southern United States. A total of 255 Salmonella isolates was obtained from clinical samples of patients admitted to the hospital between 2007 and 2015. Phenotypic resistance to 14 antibiotics surveilled by the U.S. National Antimicrobial Resistance Monitoring System was determined using a commercially available panel. Whole-genome sequencing was used to identify serotypes and genotypic AMR. The most common serotypes were Salmonella enterica serotype Newport (18%), Salmonella enterica serotype Anatum (15.2%), and Salmonella enterica serotype Braenderup (11.8%). Most (n = 219) of the isolates were pansusceptible, while 25 were multidrug resistant (≥3 antimicrobial classes). Genes encoding beta-lactam resistance, such as bla_CMY-2, bla_SHV-12, bla_CTX-M-27, and bla_TEM-1B, were detected. The qnrB2 and aac(6')-Ib-cr genes were present in isolates with reduced susceptibility to ciprofloxacin. Genes encoding resistance to gentamicin (aph(3')-Ia, aac(6')-IIc), streptomycin (strA and strB), sulfonamides (sul1), trimethoprim (dfrA), phenicols (catA), tetracyclines [tet(A) and tet(E)], and macrolides [ere(A)] were also identified. The main predicted incompatibility plasmid type was I1 (10%). Core genome-based analyses revealed phylogenetic associations between isolates of common serotypes. The presence of AMR Salmonella in equine patients increases the risk of unsuccessful treatment and causes concern for potential zoonotic transmission to attending veterinary personnel, animal caretakers, and horse owners. Understanding the epidemiology of Salmonella in horses admitted to referral hospitals is important for the prevention, control, and treatment of salmonellosis.IMPORTANCE In horses, salmonellosis is a leading cause of life-threatening colitis. At veterinary teaching hospitals, nosocomial outbreaks can increase the risk of zoonotic transmission, lead to restrictions on admissions, impact hospital reputation, and interrupt educational activities. The antimicrobials most often used in horses are included in the 5th revision of the World Health Organization's list of critically important antimicrobials for human medicine. Recent studies have demonstrated a trend of increasing bacterial resistance to drugs commonly used to treat Salmonella infections. In this study, we identify temporal trends in the distribution of Salmonella serotypes and their mechanisms of antimicrobial resistance; furthermore, we are able to determine the likely origin of several temporal clusters of infection by using whole-genome sequencing. These data can be used to focus strategies to better contain the dissemination and enhance the mitigation of Salmonella infections and to provide evidence-based policies and guidelines to steward antimicrobial use in veterinary medicine.