Evaluation of a fully automated bioinformatics tool to predict antibiotic resistance from MRSA genomes

Narender Kumar*, Kathy E. Raven, Beth Blane, Danielle Leek, Nicholas M. Brown, Eugene Bragin, Paul A. Rhodes, Julian Parkhill, Sharon Peacock

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

The genetic prediction of phenotypic antibiotic resistance based on analysis of WGS data is becoming increasingly feasible, but a major barrier to its introduction into routine use is the lack of fully automated interpretation tools. Here, we report the findings of a large evaluation of the Next Gen Diagnostics (NGD) automated bioinformatics analysis tool to predict the phenotypic resistance of MRSA. Methods: MRSA-positive patients were identified in a clinical microbiology laboratory in England between January and November 2018. One MRSA isolate per patient together with all blood culture isolates (total n = 778) were sequenced on the Illumina MiniSeq instrument in batches of 21 clinical MRSA isolates and three controls. Results: The NGD system activated post-sequencing and processed the sequences to determine susceptible/resistant predictions for 11 antibiotics, taking around 11 minutes to analyse 24 isolates sequenced on a single sequencing run. NGD results were compared with phenotypic susceptibility testing performed by the clinical laboratory using the disc diffusion method and EUCAST breakpoints. Following retesting of discrepant results, concordance between phenotypic results and NGD genetic predictions was 99.69%. Further investigation of 22 isolate genomes associated with persistent discrepancies revealed a range of reasons in 12 cases, but no cause could be found for the remainder. Genetic predictions generated by the NGD tool were compared with predictions generated by an independent research-based informatics approach, which demonstrated an overall concordance between the two methods of 99.97%. Conclusions: We conclude that the NGD system provides rapid and accurate prediction of the antibiotic susceptibility of MRSA.

Original languageEnglish
Pages (from-to)1117-1122
Number of pages6
JournalJournal of Antimicrobial Chemotherapy
Volume75
Issue number5
DOIs
Publication statusPublished - 1 May 2020

Bibliographical note

Funding Information:
This work was supported by the Health Innovation Challenge Fund (WT098600, HICF-T5-342), a parallel funding partnership between the Department of Health and Wellcome Trust. This project was also funded by a grant awarded to the Wellcome Trust Sanger Institute (098051).

Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy.

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