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.

Whole genome analysis of Gram-negative bacteria using the EPISEQ CS application and other bioinformatic platforms.

OBJECTIVES: To determine genomic characteristics and molecular epidemiology of carbapenem non-susceptible Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii, and Pseudomonas aeruginosa from medical centres of Mexico using whole genome sequencing data analysed with the EPISEQⓇ CS application and other bioinformatic platforms. METHODS: Clinical isolates collected from 28 centres in Mexico included carbapenem-non-susceptible K. pneumoniae (n = 22), E. coli (n = 24), A. baumannii (n = 16), and P. aeruginosa (n = 13). Isolates were subjected to whole genome sequencing using the Illumina (MiSeq) platform. FASTQ files were uploaded to the EPISEQⓇ CS application for analysis. Additionally, the tools Kleborate v2.0.4 and Pathogenwatch were used as comparators for Klebsiella genomes, and the bacterial whole genome sequence typing database was used for E. coli and A. baumannii. RESULTS: For K. pneumoniae, both bioinformatic approaches detected multiple genes encoding aminoglycoside, quinolone, and phenicol resistance, and the presence of blaNDM-1 explained carbapenem non-susceptibility in 18 strains and blaKPC-3 in four strains. Regarding E. coli, both EPISEQⓇ CS and bacterial whole genome sequence typing database analyses detected multiple virulence and resistance genes: 20 of 24 (83.3%) strains carried blaNDM, 3 of 24 (12.4%) carried blaOXA-232, and 1 carried blaOXA-181. Genes that confer resistance to aminoglycosides, tetracyclines, sulfonamides, phenicols, trimethoprim, and macrolides were also detected by both platforms. Regarding A. baumannii, the most frequent carbapenemase-encoding gene detected by both platforms was blaOXA-72, followed by blaOXA-66. Both approaches detected similar genes for aminoglycosides, carbapenems, tetracyclines, phenicols, and sulfonamides. Regarding P. aeruginosa, blaVIM, blaIMP, and blaGES were the more frequently detected. Multiple virulence genes were detected in all strains. CONCLUSION: Compared to the other available platforms, EPISEQⓇ CS enabled a comprehensive resistance and virulence analysis, providing a reliable method for bacterial strain typing and characterization of the virulome and resistome.

Rapid Genomic Characterization and Global Surveillance of Klebsiella Using Pathogenwatch.

BACKGROUND: Klebsiella species, including the notable pathogen K. pneumoniae, are increasingly associated with antimicrobial resistance (AMR). Genome-based surveillance can inform interventions aimed at controlling AMR. However, its widespread implementation requires tools to streamline bioinformatic analyses and public health reporting. METHODS: We developed the web application Pathogenwatch, which implements analytics tailored to Klebsiella species for integration and visualization of genomic and epidemiological data. We populated Pathogenwatch with 16 537 public Klebsiella genomes to enable contextualization of user genomes. We demonstrated its features with 1636 genomes from 4 low- and middle-income countries (LMICs) participating in the NIHR Global Health Research Unit (GHRU) on AMR. RESULTS: Using Pathogenwatch, we found that GHRU genomes were dominated by a small number of epidemic drug-resistant clones of K. pneumoniae. However, differences in their distribution were observed (eg, ST258/512 dominated in Colombia, ST231 in India, ST307 in Nigeria, ST147 in the Philippines). Phylogenetic analyses including public genomes for contextualization enabled retrospective monitoring of their spread. In particular, we identified hospital outbreaks, detected introductions from abroad, and uncovered clonal expansions associated with resistance and virulence genes. Assessment of loci encoding O-antigens and capsule in K. pneumoniae, which represent possible vaccine candidates, showed that 3 O-types (O1-O3) represented 88.9% of all genomes, whereas capsule types were much more diverse. CONCLUSIONS: Pathogenwatch provides a free, accessible platform for real-time analysis of Klebsiella genomes to aid surveillance at local, national, and global levels. We have improved representation of genomes from GHRU participant countries, further facilitating ongoing surveillance.

A community-driven resource for genomic epidemiology and antimicrobial resistance prediction of Neisseria gonorrhoeae at Pathogenwatch.

BACKGROUND: Antimicrobial-resistant (AMR) Neisseria gonorrhoeae is an urgent threat to public health, as strains resistant to at least one of the two last-line antibiotics used in empiric therapy of gonorrhoea, ceftriaxone and azithromycin, have spread internationally. Whole genome sequencing (WGS) data can be used to identify new AMR clones and transmission networks and inform the development of point-of-care tests for antimicrobial susceptibility, novel antimicrobials and vaccines. Community-driven tools that provide an easy access to and analysis of genomic and epidemiological data is the way forward for public health surveillance. METHODS: Here we present a public health-focussed scheme for genomic epidemiology of N. gonorrhoeae at Pathogenwatch ( https://pathogen.watch/ngonorrhoeae ). An international advisory group of experts in epidemiology, public health, genetics and genomics of N. gonorrhoeae was convened to inform on the utility of current and future analytics in the platform. We implement backwards compatibility with MLST, NG-MAST and NG-STAR typing schemes as well as an exhaustive library of genetic AMR determinants linked to a genotypic prediction of resistance to eight antibiotics. A collection of over 12,000 N. gonorrhoeae genome sequences from public archives has been quality-checked, assembled and made public together with available metadata for contextualization. RESULTS: AMR prediction from genome data revealed specificity values over 99% for azithromycin, ciprofloxacin and ceftriaxone and sensitivity values around 99% for benzylpenicillin and tetracycline. A case study using the Pathogenwatch collection of N. gonorrhoeae public genomes showed the global expansion of an azithromycin-resistant lineage carrying a mosaic mtr over at least the last 10 years, emphasising the power of Pathogenwatch to explore and evaluate genomic epidemiology questions of public health concern. CONCLUSIONS: The N. gonorrhoeae scheme in Pathogenwatch provides customised bioinformatic pipelines guided by expert opinion that can be adapted to public health agencies and departments with little expertise in bioinformatics and lower-resourced settings with internet connection but limited computational infrastructure. The advisory group will assess and identify ongoing public health needs in the field of gonorrhoea, particularly regarding gonococcal AMR, in order to further enhance utility with modified or new analytic methods.