For nearly 100 years serotyping has been the gold standard for the identification of Salmonella serovars. Despite the increasing adoption of DNA-based subtyping approaches, serotype information remains a cornerstone in food safety and public health activities aimed at reducing the burden of salmonellosis. At the same time, recent advances in whole-genome sequencing (WGS) promise to revolutionize our ability to perform advanced pathogen characterization in support of improved source attribution and outbreak analysis. We present the Salmonella In Silico Typing Resource (SISTR), a bioinformatics platform for rapidly performing simultaneous in silico analyses for several leading subtyping methods on draft Salmonella genome assemblies. In addition to performing serovar prediction by genoserotyping, this resource integrates sequence-based typing analyses for: Multi-Locus Sequence Typing (MLST), ribosomal MLST (rMLST), and core genome MLST (cgMLST). We show how phylogenetic context from cgMLST analysis can supplement the genoserotyping analysis and increase the accuracy of in silico serovar prediction to over 94.6% on a dataset comprised of 4,188 finished genomes and WGS draft assemblies. In addition to allowing analysis of user-uploaded whole-genome assemblies, the SISTR platform incorporates a database comprising over 4,000 publicly available genomes, allowing users to place their isolates in a broader phylogenetic and epidemiological context. The resource incorporates several metadata driven visualizations to examine the phylogenetic, geospatial and temporal distribution of genome-sequenced isolates. As sequencing of Salmonella isolates at public health laboratories around the world becomes increasingly common, rapid in silico analysis of minimally processed draft genome assemblies provides a powerful approach for molecular epidemiology in support of public health investigations. Moreover, this type of integrated analysis using multiple sequence-based methods of sub-typing allows for continuity with historical serotyping data as we transition towards the increasing adoption of genomic analyses in epidemiology. The SISTR platform is freely available on the web at https://lfz.corefacility.ca/sistr-app/.
Salmonella serotyping remains the gold-standard tool for the classification of Salmonella isolates and forms the basis of Canada’s national surveillance program for this priority foodborne pathogen. Public health officials have been increasingly looking toward whole genome sequencing (WGS) to provide a large set of data from which all the relevant information about an isolate can be mined. However, rigorous validation and careful consideration of potential implications in the replacement of traditional surveillance methodologies with WGS data analysis tools is needed. Two in silico tools for Salmonella serotyping have been developed, the Salmonella in silico Typing Resource (SISTR) and SeqSero, while seven gene MLST for serovar prediction can be adapted for in silico analysis. All three analysis methods were assessed and compared to traditional serotyping techniques using a set of 813 verified clinical and laboratory isolates, including 492 Canadian clinical isolates and 321 isolates of human and non-human sources. Successful results were obtained for 94.8, 88.2, and 88.3% of the isolates tested using SISTR, SeqSero, and MLST, respectively, indicating all would be suitable for maintaining historical records, surveillance systems, and communication structures currently in place and the choice of the platform used will ultimately depend on the users need. Results also pointed to the need to reframe serotyping in the genomic era as a test to understand the genes that are carried by an isolate, one which is not necessarily congruent with what is antigenically expressed. The adoption of WGS for serotyping will provide the simultaneous collection of information that can be used by multiple programs within the current surveillance paradigm; however, this does not negate the importance of the various programs or the role of serotyping going forward.
Public health and food safety institutions around the world are adopting whole genome sequencing (WGS) to replace conventional methods for characterizing Salmonella for use in surveillance and outbreak response. Falling costs and increased throughput of WGS have resulted in an explosion of data, but questions remain as to the reliability and robustness of the data. Due to the critical importance of serovar information to public health, it is essential to have reliable serovar assignments available for all of the Salmonella records. The current study used a systematic assessment and curation of all Salmonella in the sequence read archive (SRA) to assess the state of the data and their utility. A total of 67 758 genomes were assembled de novo and quality-assessed for their assembly metrics as well as species and serovar assignments. A total of 42 400 genomes passed all of the quality criteria but 30.16 % of genomes were deposited without serotype information. These data were used to compare the concordance of reported and predicted serovars for two in silico prediction tools, multi-locus sequence typing (MLST) and the Salmonella in silico Typing Resource (SISTR), which produced predictions that were fully concordant with 87.51 and 91.91 % of the tested isolates, respectively. Concordance of in silico predictions increased when serovar variants were grouped together, 89.25 % for MLST and 94.98 % for SISTR. This study represents the first large-scale validation of serovar information in public genomes and provides a large validated set of genomes, which can be used to benchmark new bioinformatics tools.
We have developed a Salmonella genoserotyping array (SGSA) which rapidly generates an antigenic formula consistent with the White-Kauffmann-Le Minor scheme, currently the gold standard for Salmonella serotyping. A set of 287 strains representative of 133 Salmonella serovars was assembled to validate the array and to test the array probes for accuracy, specificity, and reproducibility. Initially, 76 known serovars were utilized to validate the specificity and repeatability of the array probes and their expected probe patterns. The SGSA generated the correct serovar designations for 100% of the known subspecies I serovars tested in the validation panel and an antigenic formula consistent with that of the White-Kauffmann-Le Minor scheme for 97% of all known serovars tested. Once validated, the SGSA was assessed against a blind panel of 100 Salmonella enterica subsp. I samples serotyped using traditional methods. In summary, the SGSA correctly identified all of the blind samples as representing Salmonella and successfully identified 92% of the antigens found within the unknown samples. Antigen-and serovar-specific probes, in combination with a pepT PCR for confirmation of S. enterica subsp. Enteritidis determinations, generated an antigenic formula and/or a serovar designation consistent with the White-Kauffmann-Le Minor scheme for 87% of unknown samples tested with the SGSA. Future experiments are planned to test the specificity of the array probes with other Salmonella serovars to demonstrate the versatility and utility of this array as a public health tool in the identification of Salmonella.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.