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Benchmarking of two bioinformatic workflows for the analysis of whole-genome sequenced Staphylococcus aureus collected from patients with suspected sepsis
Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Sweden.
University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. (Infection Biology)ORCID iD: 0000-0002-8181-4131
University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. (Infection Biology)ORCID iD: 0000-0003-3747-5950
University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. (Infection Biology)
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2023 (English)In: BMC Infectious Diseases, E-ISSN 1471-2334, Vol. 23, no 1, p. 39-, article id 39Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: The rapidly growing area of sequencing technologies, and more specifically bacterial whole-genome sequencing, could offer applications in clinical microbiology, including species identification of bacteria, prediction of genetic antibiotic susceptibility and virulence genes simultaneously. To accomplish the aforementioned points, the commercial cloud-based platform, 1928 platform (1928 Diagnostics, Gothenburg, Sweden) was benchmarked against an in-house developed bioinformatic pipeline as well as to reference methods in the clinical laboratory.

METHODS: Whole-genome sequencing data retrieved from 264 Staphylococcus aureus isolates using the Illumina HiSeq X next-generation sequencing technology was used. The S. aureus isolates were collected during a prospective observational study of community-onset severe sepsis and septic shock in adults at Skaraborg Hospital, in the western region of Sweden. The collected isolates were characterized according to accredited laboratory methods i.e., species identification by MALDI-TOF MS analysis and phenotypic antibiotic susceptibility testing (AST) by following the EUCAST guidelines. Concordance between laboratory methods and bioinformatic tools, as well as concordance between the bioinformatic tools was assessed by calculating the percent of agreement.

RESULTS: There was an overall high agreement between predicted genotypic AST and phenotypic AST results, 98.0% (989/1006, 95% CI 97.3-99.0). Nevertheless, the 1928 platform delivered predicted genotypic AST results with lower very major error rates but somewhat higher major error rates compared to the in-house pipeline. There were differences in processing times i.e., minutes versus hours, where the 1928 platform delivered the results faster. Furthermore, the bioinformatic workflows showed overall 99.4% (1267/1275, 95% CI 98.7-99.7) agreement in genetic prediction of the virulence gene characteristics and overall 97.9% (231/236, 95% CI 95.0-99.2%) agreement in predicting the sequence types (ST) of the S. aureus isolates.

CONCLUSIONS: Altogether, the benchmarking disclosed that both bioinformatic workflows are able to deliver results with high accuracy aiding diagnostics of severe infections caused by S. aureus. It also illustrates the need of international agreement on quality control and metrics to facilitate standardization of analytical approaches for whole-genome sequencing based predictions.

Place, publisher, year, edition, pages
BioMed Central (BMC), 2023. Vol. 23, no 1, p. 39-, article id 39
Keywords [en]
Antimicrobial susceptibility, Benchmarking, Clinical microbiology, Illumina sequencing, S. aureus, Species identification, Virulence genes, Whole-genome sequencing
National Category
Microbiology Bioinformatics and Systems Biology Infectious Medicine Microbiology in the medical area Genetics
Research subject
Infection Biology
Identifiers
URN: urn:nbn:se:his:diva-22199DOI: 10.1186/s12879-022-07977-0ISI: 000921125300004PubMedID: 36670352Scopus ID: 2-s2.0-85146795212OAI: oai:DiVA.org:his-22199DiVA, id: diva2:1729753
Funder
Knowledge Foundation, 206/0330Knowledge Foundation, 2017/14
Note

CC BY 4.0

The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/ zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Correspondence: Anna‑Karin Pernestig anna‑karin.pernestig@his.se

Open access funding provided by University of Skövde. Swedish Knowledge Foundation BioMine Grant No. 206/0330, Swedish Knowledge Foundation Associate Senior Lecturer in Systems biology, Grant No. 2017/14, Stiftelsen Tornspiran, Internal research fund, Unilabs AB.

The datasets generated and/or analysed during the current study are available in the online NCBI repository, https://www.ncbi.nlm.nih.gov/, BioProject PRJNA606666, http://www.ncbi.nlm.nih.gov/bioproject/606666

Available from: 2023-01-23 Created: 2023-01-23 Last updated: 2024-01-17Bibliographically approved

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Tilevik, DianaTilevik, AndreasJurcevic, SanjaEnroth, HelenaPernestig, Anna-Karin

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