Comparison of next generation technologies and bioinformatics pipelines for capsular typing of Streptococcus pneumoniae.

Whole genome sequencing (WGS)-based approaches for pneumococcal capsular typing have become an alternative to serological methods. In silico serotyping from WGS has not yet been applied to long-read sequences produced by third-generation technologies. The objective of the study was to determine the capsular types of pneumococci causing invasive disease in Catalonia (Spain) using serological typing and WGS and to compare the performance of different bioinformatics pipelines using short- and long-read data from WGS. All invasive pneumococcal pediatric isolates collected in Hospital Sant Joan de Déu (Barcelona) from 2013 to 2019 were included. Isolates were assigned a capsular type by serological testing based on anticapsular antisera and by different WGS-based pipelines: Illumina sequencing followed by serotyping with PneumoCaT, SeroBA, and Pathogenwatch vs MinION-ONT sequencing coupled with serotyping by Pathogenwatch from pneumococcal assembled genomes. A total of 119 out of 121 pneumococcal isolates were available for sequencing. Twenty-nine different serotypes were identified by serological typing, with 24F (n = 17; 14.3%), 14 (n = 10; 8.4%), and 15B/C (n = 8; 6.7%) being the most common serotypes. WGS-based pipelines showed initial concordance with serological typing (>91% of accuracy). The main discrepant results were found at the serotype level within a serogroup: 6A/B, 6C/D, 9A/V, 11A/D, and 18B/C. Only one discrepancy at the serogroup level was observed: serotype 29 by serological testing and serotype 35B/D by all WGS-based pipelines. Thus, bioinformatics WGS-based pipelines, including those using third-generation sequencing, are useful for pneumococcal capsular assignment. Possible discrepancies between serological typing and WGS-based approaches should be considered in pneumococcal capsular-type surveillance studies.

ShigaPass: an in silico tool predicting Shigella serotypes from whole-genome sequencing assemblies.

Shigella is one of the commonest causes of diarrhoea worldwide and a major public health problem. Shigella serotyping is based on a standardized scheme that splits Shigella strains into four serogroups and 60 serotypes on the basis of biochemical tests and O-antigen structures. This conventional serotyping method is laborious, time-consuming, impossible to automate, and requires a high level of expertise. Whole-genome sequencing (WGS) is becoming more affordable and is now used for routine surveillance, opening up possibilities for the development of much-needed accurate rapid typing methods. Here, we describe ShigaPass, a new in silico tool for predicting Shigella serotypes from WGS assemblies on the basis of rfb gene cluster DNA sequences, phage and plasmid-encoded O-antigen modification genes, seven housekeeping genes (EnteroBase's MLST scheme), fliC alleles and clustered regularly interspaced short palindromic repeats (CRISPR) spacers. Using 4879 genomes, including 4716 reference strains and clinical isolates of Shigella characterized with a panel of biochemical tests and serotyped by slide agglutination, we show here that ShigaPass outperforms all existing in silico tools, particularly for the identification of Shigella boydii and Shigella dysenteriae serotypes, with a correct serotype assignment rate of 98.5 % and a sensitivity rate (i.e. ability to make any prediction) of 100 %.

SSP: An In Silico Tool for Salmonella Species Serotyping Using the Sequences of O-Antigen Biosynthesis Proteins and H-Antigen Filament Proteins.

Over 2500 Salmonella species (alternatively, serovars) encompassing different combinations of O-, H1- and H2-antigens are present in nature and cause millions of deaths worldwide every year. Since conventional serotyping is time-consuming, a user-friendly Salmonellaspecies serotyping (SSP) web tool (https://project.iith.ac.in/SSP/) is developed here to predict the serotypes using Salmonella protein(s) or whole proteome sequences. Prior to SSP implementation, a detailed analysis of protein sequences involved in O-antigen biosynthesis and H-antigen formation is carried out to assess their serotype specificity. Intriguingly, the results indicate that the initializing transferases WbaP, WecA and GNE can efficiently distinguish the O-antigens, which have Gal, GlcNAc and GalNAc as initial sugars respectively. Rigorous analysis shows that Wzx and Wzy are sufficient to distinguish the O-types. Exceptionally, some situations warrant additional proteins. Thus, 150 additional transferases, RfbE for O2, O9 and O9,46 types, Orf17.4 for O3,10 and O1,3,19 types, WecB, WbbE and WbbF for O54 and, Wzm and Wzt for O67 are utilized in serotyping. An in-depth analysis of 302 reference datasets representing 56 H1- and 20 H2-types leads to the identification and utilization of 61 unique sequence patterns of FliC and FljB in H-typing. A test dataset of 2136 whole proteome sequences covering 740 Salmonella serovars, including 13 new species are successfully predicted with 99.72% accuracy. Prior to this, all the O-, H1- and H2-antigens are predicted accurately when tested independently. Indeed, SSP also identifies wrongly annotated Salmonella species; hence, it can easily identify new species that emerge with any combination of O-, H1- and H2-antigens. Thus, SSP can act as a valuable tool in the surveillance of Salmonella species.

Oxford nanopore technologies-a valuable tool to generate whole-genome sequencing data for in silico serotyping and the detection of genetic markers in Salmonella.

Bacteria of the genus Salmonella pose a major risk to livestock, the food economy, and public health. Salmonella infections are one of the leading causes of food poisoning. The identification of serovars of Salmonella achieved by their diverse surface antigens is essential to gain information on their epidemiological context. Traditionally, slide agglutination has been used for serotyping. In recent years, whole-genome sequencing (WGS) followed by in silico serotyping has been established as an alternative method for serotyping and the detection of genetic markers for Salmonella. Until now, WGS data generated with Illumina sequencing are used to validate in silico serotyping methods. Oxford Nanopore Technologies (ONT) opens the possibility to sequence ultra-long reads and has frequently been used for bacterial sequencing. In this study, ONT sequencing data of 28 Salmonella strains of different serovars with epidemiological relevance in humans, food, and animals were taken to investigate the performance of the in silico serotyping tools SISTR and SeqSero2 compared to traditional slide agglutination tests. Moreover, the detection of genetic markers for resistance against antimicrobial agents, virulence, and plasmids was studied by comparing WGS data based on ONT with WGS data based on Illumina. Based on the ONT data from flow cell version R9.4.1, in silico serotyping achieved an accuracy of 96.4 and 92% for the tools SISTR and SeqSero2, respectively. Highly similar sets of genetic markers comparing both sequencing technologies were identified. Taking the ongoing improvement of basecalling and flow cells into account, ONT data can be used for Salmonella in silico serotyping and genetic marker detection.

ECTyper: in silico Escherichia coli serotype and species prediction from raw and assembled whole-genome sequence data.

Escherichia coli is a priority foodborne pathogen of public health concern and phenotypic serotyping provides critical information for surveillance and outbreak detection activities. Public health and food safety laboratories are increasingly adopting whole-genome sequencing (WGS) for characterizing pathogens, but it is imperative to maintain serotype designations in order to minimize disruptions to existing public health workflows. Multiple in silico tools have been developed for predicting serotypes from WGS data, including SRST2, SerotypeFinder and EToKi EBEis, but these tools were not designed with the specific requirements of diagnostic laboratories, which include: speciation, input data flexibility (fasta/fastq), quality control information and easily interpretable results. To address these specific requirements, we developed ECTyper (https://github.com/phac-nml/ecoli_serotyping) for performing both speciation within Escherichia and Shigella, and in silico serotype prediction. We compared the serotype prediction performance of each tool on a newly sequenced panel of 185 isolates with confirmed phenotypic serotype information. We found that all tools were highly concordant, with 92-97 % for O-antigens and 98-100 % for H-antigens, and ECTyper having the highest rate of concordance. We extended the benchmarking to a large panel of 6954 publicly available E. coli genomes to assess the performance of the tools on a more diverse dataset. On the public data, there was a considerable drop in concordance, with 75-91 % for O-antigens and 62-90 % for H-antigens, and ECTyper and SerotypeFinder being the most concordant. This study highlights that in silico predictions show high concordance with phenotypic serotyping results, but there are notable differences in tool performance. ECTyper provides highly accurate and sensitive in silico serotype predictions, in addition to speciation, and is designed to be easily incorporated into bioinformatic workflows.

Serotypes, Virulence-Associated Factors, and Antimicrobial Resistance of Streptococcus suis Isolates Recovered From Sick and Healthy Pigs Determined by Whole-Genome Sequencing.

Streptococcus suis is ubiquitous in swine, and yet, only a small percentage of pigs become clinically ill. The objective of this study was to describe the distribution of serotypes, virulence-associated factor (VAF), and antimicrobial resistance (AMR) genes in S. suis isolates recovered from systemic (blood, meninges, spleen, and lymph node) and non-systemic (tonsil, nasal cavities, ileum, and rectum) sites of sick and healthy pigs using whole-genome sequencing. In total, 273 S. suis isolates recovered from 112 pigs (47 isolates from systemic and 136 from non-systemic sites of 65 sick pigs; 90 isolates from non-systemic sites of 47 healthy pigs) on 17 Ontario farms were subjected to whole-genome sequencing. Using in silico typing, 21 serotypes were identified with serotypes 9 (13.9%) and 2 (8.4%) as the most frequent serotypes, whereas 53 (19.4%) isolates remained untypable. The relative frequency of VAF genes in isolates from systemic (Kruskal-Wallis, p < 0.001) and non-systemic (Kruskal-Wallis, p < 0.001) sites in sick pigs was higher compared with isolates from non-systemic sites in healthy pigs. Although many VAF genes were abundant in all isolates, three genes, including dltA [Fisher's test (FT), p < 0.001], luxS (FT, p = 0.01), and troA (FT, p = 0.02), were more prevalent in isolates recovered from systemic sites compared with non-systemic sites of pigs. Among the isolates, 98% had at least one AMR gene, and 79% had genes associated with at least four drug classes. The most frequently detected AMR genes were tetO conferring resistance to tetracycline and ermB conferring resistance to macrolide, lincosamide, and streptogramin. The wide distribution of VAFs genes in S. suis isolates in this study suggests that other host and environmental factors may contribute to S. suis disease development.