Whole genome sequencing of Streptococcus pneumoniae serotype 33C causing fatal sepsis in a hospitalized patient with nephrotic syndrome

ABSTRACTStreptococcus pneumoniae is responsible for 240,000 - 460,000 deaths in children under 5 years of age each year. Accurate identification of pneumococcal serotypes is important for tracking the distribution and evolution of serotypes following the introduction of effective vaccines. Recent efforts have been made to infer serotypes directly from genomic data but current software approaches are limited and do not scale well. Here, we introduce a novel method, SeroBA, which uses a hybrid assembly and mapping approach. We compared SeroBA against real and simulated data and present results on the concordance and computational performance against a validation dataset, the robustness and scalability when analysing a large dataset, and the impact of varying the depth of coverage in the cps locus region on sequence-based serotyping. SeroBA can predict serotypes, by identifying the cps locus, directly from raw whole genome sequencing read data with 98% concordance using a k-mer based method, can process 10,000 samples in just over 1 day using a standard server and can call serotypes at a coverage as low as 10x. SeroBA is implemented in Python3 and is freely available under an open source GPLv3 license from: https://github.com/sanger-pathogens/seroba.DATA SUMMARYThe reference genome Streptococcus pneumoniae ATCC 700669 is available from National Center for Biotechnology Information (NCBI) with the accession number: FM211187Simulated paired end reads for experiment 2 have been deposited in FigShare: https://doi.org/10.6084/m9.figshare.5086054.v1Accession numbers for all other experiments are listed in Supplementary Table S1 and Supplementary Table S2.I/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. ⊠IMPACT STATEMENTThis article describes SeroBA, a A-mer based method for predicting the serotypes of Streptococcus pneumoniae from Whole Genome Sequencing (WGS) data. SeroBA can identify 92 serotypes and 2 subtypes with constant memory usage and low computational costs. We showed that SeroBA is able to reliably predict serotypes at a depth of coverage as low as 10x and is scalable to large datasets.

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Characterization of Streptococcus pneumoniae strains causing invasive infections using whole-genome sequencing

Journal of microbiology epidemiology immunobiology ◽

10.36233/0372-9311-2020-97-2-113-118 ◽

2020 ◽

Vol 97 (2) ◽

pp. 113-118 ◽

Cited By ~ 1

Author(s):

Konstantin O. Mironov ◽

Vitaly I. Korchagin ◽

Yuliya V. Mikhailova ◽

Yurii G. Yanushevich ◽

Andrey A. Shelenkov ◽

...

Keyword(s):

Streptococcus Pneumoniae ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Pneumococcal Infection ◽

Whole Genome ◽

Multicenter Studies ◽

Serotype 1 ◽

Invasive Infections ◽

Sequence Types

Purpose: antigenic and genetic characterization of Streptococcus pneumoniae strains isolated from patients with invasive forms of pneumococcal infection using whole-genome sequencing.Materials and Methods. The study was performed on 46 S. pneumoniae strains isolated during the PEHASus multicenter studies in 2015-2018. Sequencing was performed using Illumina protocols and equipment. The SPAdes, SeroBA, PneumoCaT software were used for data processing, as well as BIGSdb software (PubMLST.org).Results and Discussion. Whole-genome sequences of strains were obtained; the information was entered into the PubMLST database (id: 51080-51125). Ten (21%) strains were found to have serotype 3. Five (11%) strains belonged to serotype 19F and five to serogroup 6; two of them belonged to serotype 6A; one strain had 6B and 1 had 6BE serotype; 1 strain showed discordant result (6A or 6BE). Serotype 15B was identified in 3 (6.5%) strains. Serotypes 7F, 8, 9V, 14, 22F, 23F and 28A were identified in two strains each; serotypes 1, 4, 9N, 10C, 12F, 18C, 35F, 37 and 38 were found once. The proportion of strains with serotypes included in PCV13 and PPV23 vaccines was 65% and 80%, respectively. 36 sequence types were found in strains; out of them, 6 sequence types were found for the first time. A dominant sequence type or clone complexes could not be identified using multilocus sequence typing except for serotype 3 strains. The inability to identify clonal complexes is in congruence with the previously obtained data on the absence of S. pneumoniae clones associated with pneumococcal meningitis in Russia.Conclusion. The information about serotypes of S. pneumoniae causing invasive infections together with epidemiologic data about strain sources and vaccination allows us to evaluate the effectiveness of pneumococcal vaccines and provide information for improving the PCR-based routine serotyping.

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